diff --git a/causalpy/__init__.py b/causalpy/__init__.py index 5587fb3e..09384669 100644 --- a/causalpy/__init__.py +++ b/causalpy/__init__.py @@ -41,4 +41,5 @@ "RegressionKink", "skl_models", "SyntheticControl", + "variable_selection_priors", ] diff --git a/causalpy/experiments/instrumental_variable.py b/causalpy/experiments/instrumental_variable.py index 15427b40..4fdf7429 100644 --- a/causalpy/experiments/instrumental_variable.py +++ b/causalpy/experiments/instrumental_variable.py @@ -51,6 +51,16 @@ class InstrumentalVariable(BaseExperiment): If priors are not specified we will substitute MLE estimates for the beta coefficients. Example: ``priors = {"mus": [0, 0], "sigmas": [1, 1], "eta": 2, "lkj_sd": 2}``. + vs_prior_type : str or None, default=None + Type of variable selection prior: 'spike_and_slab', 'horseshoe', or None. + If None, uses standard normal priors. + vs_hyperparams : dict, optional + Hyperparameters for variable selection priors. Only used if vs_prior_type + is not None. + binary_treatment : bool, default=False + A indicator for whether the treatment to be modelled is binary or not. + Determines which PyMC model we use to model the joint outcome and + treatment. Example -------- @@ -85,6 +95,16 @@ class InstrumentalVariable(BaseExperiment): ... formula=formula, ... model=InstrumentalVariableRegression(sample_kwargs=sample_kwargs), ... ) + >>> # With variable selection + >>> iv = cp.InstrumentalVariable( + ... instruments_data=instruments_data, + ... data=data, + ... instruments_formula=instruments_formula, + ... formula=formula, + ... model=InstrumentalVariableRegression(sample_kwargs=sample_kwargs), + ... vs_prior_type="spike_and_slab", + ... vs_hyperparams={"slab_sigma": 5.0}, + ... ) """ supports_ols = False @@ -98,6 +118,9 @@ def __init__( formula: str, model: BaseExperiment | None = None, priors: dict | None = None, + vs_prior_type=None, + vs_hyperparams=None, + binary_treatment=False, **kwargs: dict, ) -> None: super().__init__(model=model) @@ -107,6 +130,9 @@ def __init__( self.formula = formula self.instruments_formula = instruments_formula self.model = model + self.vs_prior_type = vs_prior_type + self.vs_hyperparams = vs_hyperparams or {} + self.binary_treatment = binary_treatment self.input_validation() y, X = dmatrices(formula, self.data) @@ -130,15 +156,33 @@ def __init__( COORDS = {"instruments": self.labels_instruments, "covariates": self.labels} self.coords = COORDS if priors is None: - priors = { - "mus": [self.ols_beta_first_params, self.ols_beta_second_params], - "sigmas": [1, 1], - "eta": 2, - "lkj_sd": 1, - } + if binary_treatment: + # Different default priors for binary treatment + priors = { + "mus": [self.ols_beta_first_params, self.ols_beta_second_params], + "sigmas": [1, 1], + "sigma_U": 1.0, + "rho_bounds": [-0.99, 0.99], + } + else: + # Original continuous treatment priors + priors = { + "mus": [self.ols_beta_first_params, self.ols_beta_second_params], + "sigmas": [1, 1], + "eta": 2, + "lkj_sd": 1, + } self.priors = priors self.model.fit( # type: ignore[call-arg,union-attr] - X=self.X, Z=self.Z, y=self.y, t=self.t, coords=COORDS, priors=self.priors + X=self.X, + Z=self.Z, + y=self.y, + t=self.t, + coords=COORDS, + priors=self.priors, + vs_prior_type=vs_prior_type, + vs_hyperparams=vs_hyperparams, + binary_treatment=self.binary_treatment, ) def input_validation(self) -> None: @@ -159,9 +203,8 @@ def input_validation(self) -> None: if check_binary: warnings.warn( """Warning. The treatment variable is not Binary. - This is not necessarily a problem but it violates - the assumption of a simple IV experiment. - The coefficients should be interpreted appropriately.""" + We will use the multivariate normal likelihood + for continuous treatment.""" ) def get_2SLS_fit(self) -> None: diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py index 9adc3bcf..71f5210e 100644 --- a/causalpy/pymc_models.py +++ b/causalpy/pymc_models.py @@ -27,6 +27,7 @@ from pymc_extras.prior import Prior from causalpy.utils import round_num +from causalpy.variable_selection_priors import VariableSelectionPrior class PyMCModel(pm.Model): @@ -679,7 +680,10 @@ def build_model( # type: ignore y: np.ndarray, t: np.ndarray, coords: Dict[str, Any], - priors: Dict[str, Any], + priors, + vs_prior_type=None, + vs_hyperparams=None, + binary_treatment=False, ) -> None: """Specify model with treatment regression and focal regression data and priors. @@ -702,48 +706,126 @@ def build_model( # type: ignore Dictionary of priors for the mus and sigmas of both regressions. Example: ``priors = {"mus": [0, 0], "sigmas": [1, 1], "eta": 2, "lkj_sd": 2}``. + vs_prior_type: An optional string. Can be "spike_and_slab" + or "horseshoe" or "normal + vs_hyperparams: An optional dictionary of priors for the + variable selection hyperparameters + binary_treatment: A flag for determining the relevant + likelihood to be used. + """ # --- Priors --- with self: self.add_coords(coords) - beta_t = pm.Normal( - name="beta_t", - mu=priors["mus"][0], - sigma=priors["sigmas"][0], - dims="instruments", - ) - beta_z = pm.Normal( - name="beta_z", - mu=priors["mus"][1], - sigma=priors["sigmas"][1], - dims="covariates", - ) - sd_dist = pm.Exponential.dist(priors["lkj_sd"], shape=2) - chol, corr, sigmas = pm.LKJCholeskyCov( - name="chol_cov", - eta=priors["eta"], - n=2, - sd_dist=sd_dist, - ) - # compute and store the covariance matrix - pm.Deterministic(name="cov", var=pt.dot(l=chol, r=chol.T)) - - # --- Parameterization --- - mu_y = pm.Deterministic(name="mu_y", var=pt.dot(X, beta_z)) - # focal regression - mu_t = pm.Deterministic(name="mu_t", var=pt.dot(Z, beta_t)) - # instrumental regression - mu = pm.Deterministic(name="mu", var=pt.stack(tensors=(mu_y, mu_t), axis=1)) - - # --- Likelihood --- - pm.MvNormal( - name="likelihood", - mu=mu, - chol=chol, - observed=np.stack(arrays=(y.flatten(), t.flatten()), axis=1), - shape=(X.shape[0], 2), - ) + + if vs_prior_type and ("mus" in priors or "sigmas" in priors): + warnings.warn( + "Variable selection priors specified. " + "The 'mus' and 'sigmas' in the priors dict will be ignored " + "for beta coefficients. Only 'eta' and 'lkj_sd' will be used." + ) + + # Create coefficient priors + if vs_prior_type: + # Use variable selection priors + self.vs_prior_treatment = VariableSelectionPrior( + vs_prior_type, vs_hyperparams + ) + self.vs_prior_outcome = VariableSelectionPrior( + vs_prior_type, vs_hyperparams + ) + + beta_t = self.vs_prior_treatment.create_prior( + name="beta_t", n_params=Z.shape[1], dims="instruments", X=Z + ) + + beta_z = self.vs_prior_outcome.create_prior( + name="beta_z", n_params=X.shape[1], dims="covariates", X=X + ) + else: + # Use standard normal priors + beta_t = pm.Normal( + name="beta_t", + mu=priors["mus"][0], + sigma=priors["sigmas"][0], + dims="instruments", + ) + beta_z = pm.Normal( + name="beta_z", + mu=priors["mus"][1], + sigma=priors["sigmas"][1], + dims="covariates", + ) + + if binary_treatment: + # Binary treatment formulation with correlated latent errors + sigma_U = pm.Exponential("sigma_U", priors.get("sigma_U", 1.0)) + + # Correlation parameter with bounds + rho_lower = priors.get("rho_bounds", [-0.99, 0.99])[0] + rho_upper = priors.get("rho_bounds", [-0.99, 0.99])[1] + + # Use tanh transform to keep correlation in valid range + rho_unconstr = pm.Normal("rho_unconstr", 0, 0.5) + rho = pm.Deterministic("rho", pm.math.tanh(rho_unconstr)) + + # Clip to ensure numerical stability + rho_clipped = pt.clip(rho, rho_lower + 0.01, rho_upper - 0.01) + + # Cholesky decomposition for correlated errors + inverse_rho = pm.math.sqrt(pm.math.maximum(1 - rho_clipped**2, 1e-12)) + chol = pt.stack([[sigma_U, 0.0], [sigma_U * rho_clipped, inverse_rho]]) + + # Draw latent errors + eps_raw = pm.Normal("eps_raw", 0, 1, shape=(X.shape[0], 2)) + eps = pm.Deterministic("eps", pt.dot(eps_raw, chol.T)) + + U = eps[:, 0] # Outcome error + V = eps[:, 1] # Treatment error + + # Treatment equation (logit link for binary treatment) + mu_treatment = pm.Deterministic("mu_t", pt.dot(Z, beta_t) + V) + p_t = pm.math.invlogit(mu_treatment) + pm.Bernoulli("likelihood_treatment", p=p_t, observed=t.flatten()) + + # Outcome equation + mu_outcome = pm.Deterministic("mu_y", pt.dot(X, beta_z) + U) + pm.Normal( + "likelihood_outcome", + mu=mu_outcome, + sigma=sigma_U, + observed=y.flatten(), + ) + + else: + sd_dist = pm.Exponential.dist(priors["lkj_sd"], shape=2) + chol, _, _ = pm.LKJCholeskyCov( + name="chol_cov", + eta=priors["eta"], + n=2, + sd_dist=sd_dist, + ) + # compute and store the covariance matrix + pm.Deterministic(name="cov", var=pt.dot(l=chol, r=chol.T)) + + # --- Parameterization --- + mu_y = pm.Deterministic(name="mu_y", var=pt.dot(X, beta_z)) + # focal regression + mu_t = pm.Deterministic(name="mu_t", var=pt.dot(Z, beta_t)) + # instrumental regression + mu = pm.Deterministic( + name="mu", var=pt.stack(tensors=(mu_y, mu_t), axis=1) + ) + + # --- Likelihood --- + pm.MvNormal( + name="likelihood", + mu=mu, + chol=chol, + observed=np.stack(arrays=(y.flatten(), t.flatten()), axis=1), + shape=(X.shape[0], 2), + ) def sample_predictive_distribution(self, ppc_sampler: str | None = "jax") -> None: """Function to sample the Multivariate Normal posterior predictive @@ -777,50 +859,35 @@ def sample_predictive_distribution(self, ppc_sampler: str | None = "jax") -> Non ) ) - def fit( # type: ignore + def fit( # type: ignore[override] self, - X: np.ndarray, - Z: np.ndarray, - y: np.ndarray, - t: np.ndarray, - coords: Dict[str, Any], - priors: Dict[str, Any], - ppc_sampler: str | None = None, - ) -> az.InferenceData: - """Draw samples from posterior distribution and potentially from - the prior and posterior predictive distributions. - - Parameters - ---------- - X : np.ndarray - Array used to predict our outcome y. - Z : np.ndarray - Array used to predict our treatment variable t. - y : np.ndarray - Array of values representing our focal outcome y. - t : np.ndarray - Array representing the treatment variable. - coords : dict - Dictionary with coordinate names for named dimensions. - priors : dict - Dictionary of priors for the model. - ppc_sampler : str, optional - Sampler for posterior predictive distribution. Can be 'jax', - 'pymc', or None. Defaults to None, so the user can determine - if they wish to spend time sampling the posterior predictive - distribution independently. - - Returns - ------- - az.InferenceData - InferenceData object containing the samples. + X, + Z, + y, + t, + coords, + priors, + ppc_sampler=None, + vs_prior_type=None, + vs_hyperparams=None, + binary_treatment: bool = False, + ): # type: ignore[override] + """Draw samples from posterior distribution and potentially + from the prior and posterior predictive distributions. The + fit call can take values for the + ppc_sampler = ['jax', 'pymc', None] + We default to None, so the user can determine if they wish + to spend time sampling the posterior predictive distribution + independently. """ # Ensure random_seed is used in sample_prior_predictive() and # sample_posterior_predictive() if provided in sample_kwargs. # Use JAX for ppc sampling of multivariate likelihood - self.build_model(X, Z, y, t, coords, priors) + self.build_model( + X, Z, y, t, coords, priors, vs_prior_type, vs_hyperparams, binary_treatment + ) with self: self.idata = pm.sample(**self.sample_kwargs) self.sample_predictive_distribution(ppc_sampler=ppc_sampler) @@ -926,6 +993,7 @@ def fit_outcome_model( normal_outcome: bool = True, spline_component: bool = False, winsorize_boundary: float = 0.0, + spline_knots: int = 30, ) -> tuple[az.InferenceData, pm.Model]: """ Fit a Bayesian outcome model using covariates and previously estimated propensity scores. @@ -966,6 +1034,9 @@ def fit_outcome_model( If we wish to winsorize the propensity score this can be set to clip the high and low values of the propensity at 0 + winsorize_boundary and 1-winsorize_boundary + spline_knots: int, default 30 + The number of knots we use in the 0 - 1 interval to create our spline function + Returns ------- idata_outcome : arviz.InferenceData @@ -1029,11 +1100,11 @@ class initialisation. "beta_ps_spline", priors["beta_ps"][0], priors["beta_ps"][1], - size=34, + size=spline_knots + 4, ) B = dmatrix( "bs(ps, knots=knots, degree=3, include_intercept=True, lower_bound=0, upper_bound=1) - 1", - {"ps": p, "knots": np.linspace(0, 1, 30)}, + {"ps": p, "knots": np.linspace(0, 1, spline_knots)}, ) B_f = np.asarray(B, order="F") splines_summed = pm.Deterministic( diff --git a/causalpy/tests/test_integration_pymc_examples.py b/causalpy/tests/test_integration_pymc_examples.py index 00068507..6675a298 100644 --- a/causalpy/tests/test_integration_pymc_examples.py +++ b/causalpy/tests/test_integration_pymc_examples.py @@ -682,6 +682,116 @@ def test_iv_reg(mock_pymc_sample): result.get_plot_data() +@pytest.mark.integration +def test_iv_binary_treatment(mock_pymc_sample): + df = cp.load_data("risk") + df["binary_trt"] = np.random.binomial(1, 0.5, len(df)) + instruments_formula = "binary_trt ~ 1 + risk + logmort0" + formula = "loggdp ~ 1 + binary_trt + risk" + instruments_data = df[["risk", "logmort0", "binary_trt"]] + data = df[["loggdp", "risk", "binary_trt"]] + + result = cp.InstrumentalVariable( + instruments_data=instruments_data, + data=data, + instruments_formula=instruments_formula, + formula=formula, + model=cp.pymc_models.InstrumentalVariableRegression( + sample_kwargs=sample_kwargs + ), + binary_treatment=True, + ) + result.model.sample_predictive_distribution(ppc_sampler="pymc") + assert isinstance(df, pd.DataFrame) + assert isinstance(data, pd.DataFrame) + assert isinstance(instruments_data, pd.DataFrame) + assert isinstance(result, cp.InstrumentalVariable) + assert len(result.idata.posterior.coords["chain"]) == sample_kwargs["chains"] + assert len(result.idata.posterior.coords["draw"]) == sample_kwargs["draws"] + with pytest.raises(NotImplementedError): + result.get_plot_data() + assert "rho" in result.model.named_vars + + +@pytest.mark.integration +def test_iv_reg_vs_prior(mock_pymc_sample): + df = cp.load_data("risk") + instruments_formula = "risk ~ 1 + logmort0" + formula = "loggdp ~ 1 + risk" + instruments_data = df[["risk", "logmort0"]] + data = df[["loggdp", "risk"]] + + result = cp.InstrumentalVariable( + instruments_data=instruments_data, + data=data, + instruments_formula=instruments_formula, + formula=formula, + model=cp.pymc_models.InstrumentalVariableRegression( + sample_kwargs=sample_kwargs + ), + vs_prior_type="spike_and_slab", + vs_hyperparams={"pi_alpha": 5}, + ) + result.model.sample_predictive_distribution(ppc_sampler="pymc") + assert isinstance(df, pd.DataFrame) + assert isinstance(data, pd.DataFrame) + assert isinstance(instruments_data, pd.DataFrame) + assert isinstance(result, cp.InstrumentalVariable) + assert len(result.idata.posterior.coords["chain"]) == sample_kwargs["chains"] + assert len(result.idata.posterior.coords["draw"]) == sample_kwargs["draws"] + with pytest.raises(NotImplementedError): + result.get_plot_data() + assert "gamma_beta_t" in result.model.named_vars + assert "pi_beta_t" in result.model.named_vars + summary = result.model.vs_prior_outcome.get_inclusion_probabilities( + result.idata, "beta_z" + ) + assert isinstance(summary, pd.DataFrame) + with pytest.raises(ValueError): + summary = result.model.vs_prior_outcome.get_shrinkage_factors( + result.idata, "beta_z" + ) + + +@pytest.mark.integration +def test_iv_reg_vs_prior_hs(mock_pymc_sample): + df = cp.load_data("risk") + instruments_formula = "risk ~ 1 + logmort0" + formula = "loggdp ~ 1 + risk" + instruments_data = df[["risk", "logmort0"]] + data = df[["loggdp", "risk"]] + + result = cp.InstrumentalVariable( + instruments_data=instruments_data, + data=data, + instruments_formula=instruments_formula, + formula=formula, + model=cp.pymc_models.InstrumentalVariableRegression( + sample_kwargs=sample_kwargs + ), + vs_prior_type="horseshoe", + ) + result.model.sample_predictive_distribution(ppc_sampler="pymc") + assert isinstance(df, pd.DataFrame) + assert isinstance(data, pd.DataFrame) + assert isinstance(instruments_data, pd.DataFrame) + assert isinstance(result, cp.InstrumentalVariable) + assert len(result.idata.posterior.coords["chain"]) == sample_kwargs["chains"] + assert len(result.idata.posterior.coords["draw"]) == sample_kwargs["draws"] + with pytest.raises(NotImplementedError): + result.get_plot_data() + assert "tau_beta_t" in result.model.named_vars + assert "tau_beta_z" in result.model.named_vars + summary = result.model.vs_prior_outcome.get_shrinkage_factors( + result.idata, "beta_z" + ) + assert isinstance(summary, pd.DataFrame) + with pytest.raises(ValueError): + summary = result.model.vs_prior_outcome.get_inclusion_probabilities( + result.idata, "beta_z" + ) + + @pytest.mark.integration def test_inverse_prop(mock_pymc_sample): """Test the InversePropensityWeighting class.""" diff --git a/causalpy/tests/test_variable_selection_priors.py b/causalpy/tests/test_variable_selection_priors.py new file mode 100644 index 00000000..1b464be6 --- /dev/null +++ b/causalpy/tests/test_variable_selection_priors.py @@ -0,0 +1,125 @@ +# Copyright 2022 - 2025 The PyMC Labs Developers +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import pymc as pm +import pytest + +from causalpy.variable_selection_priors import ( + HorseshoePrior, + SpikeAndSlabPrior, + VariableSelectionPrior, + create_variable_selection_prior, +) + + +@pytest.fixture +def sample_data(): + """Generate sample design matrix for testing.""" + rng = np.random.default_rng(42) + n_obs = 100 + n_features = 5 + X = rng.normal(size=(n_obs, n_features)) + return X + + +@pytest.fixture +def coords(): + """Generate sample coordinates for PyMC models.""" + return {"features": [f"x_{i}" for i in range(5)]} + + +def test_create_variable_in_model_context(coords): + """Test that create_variable works in PyMC model context.""" + prior = SpikeAndSlabPrior(dims="features") + + with pm.Model(coords=coords) as model: + beta = prior.create_variable("beta") + + # Check that beta was created + assert "beta" in model.named_vars + assert beta.name == "beta" + + # Check that intermediate variables were created + assert "pi_beta" in model.named_vars + assert "beta_raw" in model.named_vars + assert "gamma_beta" in model.named_vars + + +def test_create_variable_in_model_context_horseshoe(coords): + """Test that create_variable works in PyMC model context.""" + prior = HorseshoePrior(dims="features") + + with pm.Model(coords=coords) as model: + beta = prior.create_variable("beta") + + # Check that beta was created + assert "beta" in model.named_vars + assert beta.name == "beta" + + # Check that intermediate variables were created + assert "tau_beta" in model.named_vars + assert "lambda_beta" in model.named_vars + assert "c2_beta" in model.named_vars + assert "lambda_tilde_beta" in model.named_vars + assert "beta_raw" in model.named_vars + + +def test_create_prior_spike_and_slab(coords): + """Test create_prior for spike-and-slab.""" + vs_prior = VariableSelectionPrior("spike_and_slab", hyperparams={"pi_alpha": 5}) + + with pm.Model(coords=coords) as model: + beta = vs_prior.create_prior(name="beta", n_params=5, dims="features") + + assert "beta" in model.named_vars + assert beta.name == "beta" + + +def test_create_prior_horseshoe(coords, sample_data): + """Test create_prior for horseshoe.""" + vs_prior = VariableSelectionPrior("horseshoe") + + with pm.Model(coords=coords) as model: + beta = vs_prior.create_prior( + name="beta", n_params=5, dims="features", X=sample_data + ) + + assert "beta" in model.named_vars + assert beta.name == "beta" + + +def test_create_prior_normal(coords, sample_data): + """Test create_prior for horseshoe.""" + vs_prior = VariableSelectionPrior("normal") + + with pm.Model(coords=coords) as model: + beta = vs_prior.create_prior(name="beta", n_params=5, dims="features") + + assert "beta" in model.named_vars + assert beta.name == "beta" + + +def test_convenience_function_with_custom_hyperparams(coords): + """Test convenience function with custom hyperparameters.""" + with pm.Model(coords=coords) as model: + _ = create_variable_selection_prior( + prior_type="spike_and_slab", + name="beta", + n_params=5, + dims="features", + hyperparams={"slab_sigma": 5}, + ) + + assert "beta" in model.named_vars diff --git a/causalpy/variable_selection_priors.py b/causalpy/variable_selection_priors.py new file mode 100644 index 00000000..8c8b2001 --- /dev/null +++ b/causalpy/variable_selection_priors.py @@ -0,0 +1,594 @@ +# Copyright 2022 - 2025 The PyMC Labs Developers +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Generic variable selection priors for PyMC models using pymc-extras Prior class. + +This module provides reusable prior specifications that can be applied to any +PyMC model with coefficient vectors (beta parameters). Supports spike-and-slab +and horseshoe priors for automatic variable selection and shrinkage, built on +top of the pymc-extras Prior infrastructure. +""" + +from typing import Any, Dict, Optional, Union + +import numpy as np +import pandas as pd +import pymc as pm +import pytensor.tensor as pt +from pymc_extras.prior import Prior + + +def _relaxed_bernoulli_transform( + p: Union[float, pt.TensorVariable], temperature: float = 0.1 +): + """ + Transform function for relaxed (continuous) Bernoulli distribution. + + This provides a continuous approximation to a Bernoulli distribution, + useful for gradient-based inference. As temperature → 0, this approaches + a true binary distribution. + + Parameters + ---------- + p : float or PyMC variable + Probability parameter. + temperature : float, default=0.1 + Temperature parameter (lower = more binary). + + Returns + ------- + function + Transform function that takes uniform random variable. + """ + + def transform(u): + logit_p = pt.log(p) - pt.log(1 - p) + return pm.math.sigmoid((logit_p + pt.log(u) - pt.log(1 - u)) / temperature) + + return transform + + +class SpikeAndSlabPrior: + """ + Spike-and-slab prior using pymc-extras Prior class. + + Creates a mixture prior with a point mass at zero (spike) and a diffuse + normal distribution (slab), implemented as: + + .. math:: + \beta_{j} = \gamma_{j} \cdot \beta_{j}^{\text{raw}} \\ + \beta_{j}^{\text{raw}} \sim \mathcal{N}(0, \sigma_{\text{slab}}^{2}), \qquad + \gamma_{j} \in [0,1]. + + Parameters + ---------- + pi_alpha : float, default=2 + Beta prior alpha for selection probability + pi_beta : float, default=2 + Beta prior beta for selection probability + slab_sigma : float, default=2 + Standard deviation of slab (non-zero) component + temperature : float, default=0.1 + Relaxation parameter for binary approximation (lower = more binary) + dims : str or tuple, optional + Dimension names for the coefficient vector + + Example + ------- + >>> import pymc as pm + >>> from causalpy.variable_selection_priors import SpikeAndSlabPrior + >>> spike_slab = SpikeAndSlabPrior(dims="features") + >>> coords = {"features": ["a", "b", "c", "d", "e"]} + >>> with pm.Model(coords=coords) as model: + ... beta = spike_slab.create_variable("beta") + """ + + def __init__( + self, + pi_alpha: float = 2, + pi_beta: float = 2, + slab_sigma: float = 2, + temperature: float = 0.1, + dims: Optional[Union[str, tuple]] = None, + ): + self.pi_alpha = pi_alpha + self.pi_beta = pi_beta + self.slab_sigma = slab_sigma + self.temperature = temperature + self.dims = dims if isinstance(dims, tuple) or dims is None else (dims,) + + def create_variable(self, name: str) -> pm.Deterministic: + """ + Create spike-and-slab variable. + + Parameters + ---------- + name : str + Name for the coefficient vector + + Returns + ------- + pm.Deterministic + Coefficient vector with spike-and-slab prior + """ + # Selection probability using Prior class + pi_prior = Prior("Beta", alpha=self.pi_alpha, beta=self.pi_beta) + pi = pi_prior.create_variable(f"pi_{name}") + + # Raw coefficients (slab component) using Prior class + slab_prior = Prior("Normal", mu=0, sigma=self.slab_sigma, dims=self.dims) + beta_raw = slab_prior.create_variable(f"{name}_raw") + + # Selection indicators using relaxed Bernoulli + # We use Uniform and transform it + u = pm.Uniform(f"gamma_{name}_u", 0, 1, dims=self.dims) + transform_fn = _relaxed_bernoulli_transform(pi, self.temperature) + gamma = pm.Deterministic(f"gamma_{name}", transform_fn(u), dims=self.dims) + + # Actual coefficients + return pm.Deterministic(name, gamma * beta_raw, dims=self.dims) + + +class HorseshoePrior: + """ + Regularized horseshoe prior using pymc-extras Prior class. + + Provides continuous shrinkage with heavy tails, allowing strong signals + to escape shrinkage while weak signals are dampened: + + .. math:: + \beta_{j} & = \tau \cdot \lambda_{j} \cdot \beta_{j}^{raw} \\ + \lambda_{j} & = \sqrt{ \dfrac{c^{2}\lambda_{j}^{2}}{c^{2} + \tau^{2}\lambda_{j}^{2}} } + + Parameters + ---------- + tau0 : float, optional + Global shrinkage parameter. If None, computed from data. + nu : float, default=3 + Degrees of freedom for half-t prior on tau + c2_alpha : float, default=2 + InverseGamma alpha for regularization parameter + c2_beta : float, default=2 + InverseGamma beta for regularization parameter + dims : str or tuple, optional + Dimension names for the coefficient vector + + Example + ------- + >>> import pymc as pm + >>> from causalpy.variable_selection_priors import HorseshoePrior + >>> horseshoe = HorseshoePrior(dims="features") + >>> coords = {"features": ["a", "b", "c", "d", "e"]} + >>> with pm.Model(coords=coords) as model: + ... beta = horseshoe.create_variable("beta") + """ + + def __init__( + self, + tau0: Optional[float] = None, + nu: float = 3, + c2_alpha: float = 2, + c2_beta: float = 2, + dims: Optional[Union[str, tuple]] = None, + ): + self.tau0 = tau0 + self.nu = nu + self.c2_alpha = c2_alpha + self.c2_beta = c2_beta + self.dims = dims if isinstance(dims, tuple) or dims is None else (dims,) + + def create_variable(self, name: str) -> pm.Deterministic: + """ + Create horseshoe variable. + + Parameters + ---------- + name : str + Name for the coefficient vector + + Returns + ------- + pm.Deterministic + Coefficient vector with horseshoe prior + """ + # Global shrinkage using Prior class + tau_prior = Prior("HalfStudentT", nu=self.nu, sigma=self.tau0 or 1.0) + tau = tau_prior.create_variable(f"tau_{name}") + + # Local shrinkage parameters using Prior class + lambda_prior = Prior("HalfCauchy", beta=1.0, dims=self.dims) + lambda_ = lambda_prior.create_variable(f"lambda_{name}") + + # Regularization parameter using Prior class + c2_prior = Prior("InverseGamma", alpha=self.c2_alpha, beta=self.c2_beta) + c2 = c2_prior.create_variable(f"c2_{name}") + + # Regularized local shrinkage + lambda_tilde = pm.Deterministic( + f"lambda_tilde_{name}", + pm.math.sqrt(c2 * lambda_**2 / (c2 + tau**2 * lambda_**2)), + dims=self.dims, + ) + + # Raw coefficients using Prior class + raw_prior = Prior("Normal", mu=0, sigma=1, dims=self.dims) + beta_raw = raw_prior.create_variable(f"{name}_raw") + + # Actual coefficients + return pm.Deterministic(name, beta_raw * lambda_tilde * tau, dims=self.dims) + + +class VariableSelectionPrior: + """ + Factory for creating variable selection priors on coefficient vectors. + + This class provides a unified interface for different types of variable + selection priors that can be applied to any beta coefficient in a PyMC model. + Built on top of pymc-extras Prior class for consistency and interoperability. + + Supported prior types: + - 'spike_and_slab': Mixture prior with near-zero spike and diffuse slab + - 'horseshoe': Continuous shrinkage with adaptive regularization + - 'normal': Standard normal prior (no selection, for comparison) + + Parameters + ---------- + prior_type : str + Type of prior: 'spike_and_slab', 'horseshoe', or 'normal' + hyperparams : dict, optional + Hyperparameters specific to the chosen prior type. If None, defaults are used. + + For 'spike_and_slab': + - pi_alpha: float (default=2) - Beta prior alpha for selection probability + - pi_beta: float (default=2) - Beta prior beta for selection probability + - slab_sigma: float (default=2) - SD of slab (non-zero) component + - temperature: float (default=0.1) - Relaxation parameter for binary approximation + + For 'horseshoe': + - tau0: float (default=None) - Global shrinkage, auto-computed if None + - nu: float (default=3) - Degrees of freedom for half-t prior on tau + - c2_alpha: float (default=2) - InverseGamma alpha for regularization + - c2_beta: float (default=2) - InverseGamma beta for regularization + + For 'normal': + - mu: float or array (default=0) - Prior mean + - sigma: float or array (default=1) - Prior SD + + Example + ------- + >>> import pymc as pm + >>> from causalpy.variable_selection_priors import VariableSelectionPrior + >>> # Create spike-and-slab prior + >>> vs_prior = VariableSelectionPrior("spike_and_slab") + >>> coords = {"features": ["a", "b", "c", "d", "e"]} + >>> with pm.Model(coords=coords) as model: + ... # Create coefficients with variable selection + ... beta = vs_prior.create_prior(name="beta", n_params=5, dims="features") + """ + + def __init__(self, prior_type: str, hyperparams: Optional[Dict[str, Any]] = None): + """Initialize the variable selection prior factory.""" + self.prior_type = prior_type.lower() + self.hyperparams = hyperparams or {} + + if self.prior_type not in ["spike_and_slab", "horseshoe", "normal"]: + raise ValueError( + f"Unknown prior_type: {prior_type}. " + "Must be 'spike_and_slab', 'horseshoe', or 'normal'" + ) + + # Will be set when create_prior is called + self._prior_instance = None + + def _get_default_hyperparams( + self, n_params: int, X: Optional[np.ndarray] = None + ) -> Dict[str, Any]: + """ + Get default hyperparameters for the chosen prior type. + + Parameters + ---------- + n_params : int + Number of parameters (dimension of beta vector) + X : array-like, optional + Design matrix for computing data-adaptive defaults (horseshoe only) + + Returns + ------- + dict + Default hyperparameters + """ + if self.prior_type == "spike_and_slab": + return { + "pi_alpha": 2, + "pi_beta": 2, + "slab_sigma": 2, + "temperature": 0.1, + } + + elif self.prior_type == "horseshoe": + # Compute tau0 using rule of thumb from Piironen & Vehtari (2017) + if X is not None: + p = n_params + p0 = min(5.0, p / 2) # Expected number of nonzero coefficients + sigma_est = 1.0 + n = X.shape[0] + tau0 = (p0 / (p - p0)) * (sigma_est / np.sqrt(n)) + else: + # Fallback if no data provided + tau0 = 1.0 / np.sqrt(n_params) + + return { + "tau0": tau0, + "nu": 3, + "c2_alpha": 2, + "c2_beta": 2, + } + + else: # normal + return { + "mu": 0, + "sigma": 1, + } + + def create_prior( + self, + name: str, + n_params: int, + dims: Optional[Union[str, tuple]] = None, + X: Optional[np.ndarray] = None, + hyperparams: Optional[Dict[str, Any]] = None, + ) -> Union[pm.Deterministic, pm.Distribution]: + """ + Create the specified prior on a coefficient vector. + + This is the main method to use. It creates the appropriate prior type + based on the configuration and returns the PyMC variable. + + Parameters + ---------- + name : str + Name for the coefficient vector (e.g., 'beta', 'b', 'coef') + n_params : int + Number of parameters (length of coefficient vector) + dims : str or tuple, optional + Dimension name(s) for the coefficient vector + X : array-like, optional + Design matrix for computing data-adaptive hyperparameters + (used only for horseshoe priors) + hyperparams : dict, optional + Override default hyperparameters for this specific prior instance + + Returns + ------- + PyMC variable + The coefficient vector with the specified prior + + Example + ------- + >>> import pymc as pm + >>> import pandas as pd + >>> from causalpy.variable_selection_priors import VariableSelectionPrior + >>> vs_prior = VariableSelectionPrior("spike_and_slab") + >>> coords = {"features": ["a", "b", "c", "d", "e"]} + >>> with pm.Model(coords=coords) as model: + ... beta = vs_prior.create_prior("beta", n_params=4, dims="features") + """ + # Merge instance and call-specific hyperparameters + default_hp = self._get_default_hyperparams(n_params, X) + merged_hp = {**default_hp, **self.hyperparams} + if hyperparams: + merged_hp.update(hyperparams) + + # Normalize dims + if isinstance(dims, str): + dims = (dims,) + + # Create the appropriate prior + if self.prior_type == "spike_and_slab": + self._prior_instance = SpikeAndSlabPrior( + pi_alpha=merged_hp["pi_alpha"], + pi_beta=merged_hp["pi_beta"], + slab_sigma=merged_hp["slab_sigma"], + temperature=merged_hp["temperature"], + dims=dims, + ) # type: ignore[assignment] + return self._prior_instance.create_variable(name) # type: ignore[attr-defined] + + elif self.prior_type == "horseshoe": + self._prior_instance = HorseshoePrior( + tau0=merged_hp["tau0"], + nu=merged_hp["nu"], + c2_alpha=merged_hp["c2_alpha"], + c2_beta=merged_hp["c2_beta"], + dims=dims, + ) # type: ignore[assignment] + return self._prior_instance.create_variable(name) # type: ignore[attr-defined] + + else: # normal + # Use Prior class directly for normal + normal_prior = Prior( + "Normal", mu=merged_hp["mu"], sigma=merged_hp["sigma"], dims=dims + ) + return normal_prior.create_variable(name) + + def get_inclusion_probabilities( + self, idata, param_name: str, threshold: float = 0.5 + ) -> pd.DataFrame: + """ + Extract variable inclusion probabilities from fitted model. + + Only applicable for spike-and-slab priors. Returns the posterior + probability that each coefficient is "selected" (non-zero). + + Parameters + ---------- + idata : arviz.InferenceData + Fitted model inference data + param_name : str + Name of the coefficient parameter (must match name in create_prior) + threshold : float, default=0.5 + Threshold for considering a variable "selected" + + Returns + ------- + dict + Dictionary with keys: + - 'probabilities': Array of inclusion probabilities per coefficient + - 'selected': Boolean array indicating which are selected + - 'gamma_mean': Mean of gamma (indicator) variables + + Raises + ------ + ValueError + If prior_type is not 'spike_and_slab' or gamma variables not found + + """ + if self.prior_type != "spike_and_slab": + raise ValueError( + "Inclusion probabilities only available for 'spike_and_slab' priors" + ) + + gamma_name = f"gamma_{param_name}" + + if gamma_name not in idata.posterior: + raise ValueError( + f"Could not find '{gamma_name}' in posterior. " + f"Make sure you used the correct parameter name." + ) + + import arviz as az + + # Extract gamma values + gamma = az.extract(idata.posterior[gamma_name]) + + # Compute inclusion probabilities + probabilities = (gamma > threshold).mean(dim="sample").to_array() + gamma_mean = gamma.mean(dim="sample").to_array() + selected = probabilities > threshold + + summary = { + "probabilities": probabilities, + "selected": selected, + "gamma_mean": gamma_mean, + } + probs = summary["probabilities"].T + df = pd.DataFrame(index=list(range(len(probs)))) + + df["prob"] = probs + df["selected"] = summary["selected"].T + df["gamma_mean"] = summary["gamma_mean"].T + return df + + def get_shrinkage_factors(self, idata, param_name: str) -> pd.DataFrame: + """ + Extract shrinkage factors from horseshoe prior. + + Only applicable for horseshoe priors. Returns the effective shrinkage + applied to each coefficient: κ_j = τ · λ̃_j + + Parameters + ---------- + idata : arviz.InferenceData + Fitted model inference data + param_name : str + Name of the coefficient parameter + + Returns + ------- + dict + Dictionary with keys: + - 'shrinkage_factors': Array of shrinkage factors per coefficient + - 'tau': Global shrinkage parameter + - 'lambda_tilde': Regularized local shrinkage parameters + + Raises + ------ + ValueError + If prior_type is not 'horseshoe' or required variables not found + + """ + if self.prior_type != "horseshoe": + raise ValueError("Shrinkage factors only available for 'horseshoe' priors") + + import arviz as az + + tau_name = f"tau_{param_name}" + lambda_tilde_name = f"lambda_tilde_{param_name}" + + if tau_name not in idata.posterior: + raise ValueError(f"Could not find '{tau_name}' in posterior") + if lambda_tilde_name not in idata.posterior: + raise ValueError(f"Could not find '{lambda_tilde_name}' in posterior") + + # Extract components + tau = az.extract(idata.posterior[tau_name]).to_array() + lambda_tilde = az.extract(idata.posterior[lambda_tilde_name]).to_array() + + shrinkage_factor = np.array( + [tau[0, i] * lambda_tilde[0, :, :] for i in range(len(tau))] + ) + shrinkage_factor = shrinkage_factor.mean(axis=2) + + summary = { + "shrinkage_factors": shrinkage_factor, + "tau": tau.mean(), + "lambda_tilde": lambda_tilde.mean(dim=("sample")), + } + probs = summary["shrinkage_factors"].T + df = pd.DataFrame(index=list(range(len(probs)))) + df["shrinkage_factor"] = probs + + df["lambda_tilde"] = summary["lambda_tilde"].T + df["tau"] = np.mean(tau).item() + return df + + +def create_variable_selection_prior( + prior_type: str, + name: str, + n_params: int, + dims: Optional[Union[str, tuple]] = None, + X: Optional[np.ndarray] = None, + hyperparams: Optional[Dict[str, Any]] = None, +) -> Union[pm.Deterministic, pm.Distribution]: + """ + Convenience function to create a variable selection prior in one call. + + This is a shorthand for creating a VariableSelectionPrior instance and + calling create_prior() in one step. + + Parameters + ---------- + prior_type : str + Type of prior: 'spike_and_slab', 'horseshoe', or 'normal' + name : str + Name for the coefficient vector + n_params : int + Number of parameters + dims : str or tuple, optional + Dimension name(s) + X : array-like, optional + Design matrix for data-adaptive hyperparameters + hyperparams : dict, optional + Custom hyperparameters + + Returns + ------- + PyMC variable + The coefficient vector with specified prior + + """ + vs_prior = VariableSelectionPrior(prior_type, hyperparams) + return vs_prior.create_prior(name, n_params, dims, X) diff --git a/docs/source/_static/interrogate_badge.svg b/docs/source/_static/interrogate_badge.svg index 4704ef6c..4ed4f3af 100644 --- a/docs/source/_static/interrogate_badge.svg +++ b/docs/source/_static/interrogate_badge.svg @@ -1,10 +1,10 @@ - interrogate: 95.5% + interrogate: 94.6% - + @@ -12,8 +12,8 @@ interrogate interrogate - 95.5% - 95.5% + 94.6% + 94.6% diff --git a/docs/source/notebooks/index.md b/docs/source/notebooks/index.md index c9ae0ad7..be879e33 100644 --- a/docs/source/notebooks/index.md +++ b/docs/source/notebooks/index.md @@ -65,6 +65,7 @@ rkink_pymc.ipynb iv_pymc.ipynb iv_weak_instruments.ipynb +iv_vs_priors.ipynb ::: :::{toctree} diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb new file mode 100644 index 00000000..a3faa0c1 --- /dev/null +++ b/docs/source/notebooks/iv_vs_priors.ipynb @@ -0,0 +1,2219 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "532c6736", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + } + ], + "source": [ + "import arviz as az\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pymc as pm\n", + "\n", + "import causalpy as cp\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "id": "b1b3aa75", + "metadata": {}, + "source": [ + "## Variable Selection Priors and Instrumental Variable Designs\n", + "\n", + "When building causal inference models, we often face a dilemma: we want to control for confounders to get unbiased causal estimates, but we're not always certain which variables are the true confounders. Include too few, and we risk omitted variable bias. Include too many, and we introduce noise that inflates our uncertainty or, worse, creates multicollinearity that destabilizes our estimates.\n", + "\n", + "Traditional approaches force us to make hard choices upfront—which variables to include, which to exclude. This in ideal cases should be driven by theory. But what if we could let the data help us make these decisions while still maintaining the principled probabilistic framework of Bayesian inference? This is where variable selection priors come in. Let's first simulate some data with some natural confounding structure. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "046aa8e0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2500 rows × 23 columns

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" + ], + "text/plain": [ + " Y_cont Y_bin T_cont T_bin alpha feature_0 feature_1 \\\n", + "0 3.169837 -0.170346 1.113394 0 9.236102 1.294441 0.418241 \n", + "1 10.479049 6.662990 2.272020 1 3.787487 -0.885005 0.315013 \n", + "2 7.307821 4.118982 2.062946 1 3.311157 -1.075537 1.058536 \n", + "3 9.781360 0.649647 4.043904 1 8.423450 0.969380 0.698199 \n", + "4 5.739283 6.812030 0.642417 1 6.613922 0.245569 -0.338491 \n", + "... ... ... ... ... ... ... ... \n", + "2495 -5.099912 -0.870746 -1.409722 0 6.565630 0.226252 1.589653 \n", + "2496 -32.742858 -7.337551 -8.468435 0 4.760520 -0.495792 0.546002 \n", + "2497 6.759804 1.912040 1.615921 0 10.445934 1.778373 0.097808 \n", + "2498 -11.249395 -1.938808 -3.103529 0 5.715321 -0.113872 0.747480 \n", + "2499 21.658258 7.675401 5.660952 1 5.741192 -0.103523 -1.083828 \n", + "\n", + " feature_2 feature_3 feature_4 ... feature_8 feature_9 feature_10 \\\n", + "0 0.536286 -0.615573 -1.173784 ... 0.559393 1.111766 -0.216069 \n", + "1 0.810138 1.137214 0.203685 ... -0.017179 0.410818 0.670074 \n", + "2 1.908944 1.122914 0.611691 ... 0.050641 1.245489 -1.070642 \n", + "3 0.314419 1.446987 -2.729092 ... -1.052429 1.143079 -1.757701 \n", + "4 0.814305 -0.859798 0.334968 ... -0.547622 0.778724 0.678956 \n", + "... ... ... ... ... ... ... ... \n", + "2495 0.056005 -0.386026 -0.462251 ... 0.987633 0.246870 -0.202917 \n", + "2496 0.209072 0.666614 0.400847 ... -0.798775 -0.616483 0.431552 \n", + "2497 -0.807658 0.380358 -0.455391 ... 0.668040 1.471963 0.573966 \n", + "2498 1.635159 -1.136585 -0.007239 ... -2.404202 -2.074570 0.022878 \n", + "2499 0.896827 0.146243 1.363973 ... -1.310958 -0.004224 0.206620 \n", + "\n", + " feature_11 feature_12 feature_13 Y_cont_scaled Y_bin_scaled \\\n", + "0 0.451496 -0.863189 0.319180 0.268475 -0.438362 \n", + "1 1.954944 0.888255 -0.506518 0.916637 1.321011 \n", + "2 -0.060250 -1.857638 0.806913 0.635421 0.666008 \n", + "3 -1.167276 -0.164620 1.687004 0.854768 -0.227239 \n", + "4 1.715229 -0.439130 -0.238847 0.496327 1.359385 \n", + "... ... ... ... ... ... \n", + "2495 0.178579 0.763186 0.527462 -0.464865 -0.618693 \n", + "2496 1.238957 0.957759 -0.583051 -2.916171 -2.283696 \n", + "2497 -0.288768 -0.861025 0.372657 0.586824 0.097788 \n", + "2498 1.345018 0.705361 0.414329 -1.010186 -0.893686 \n", + "2499 0.012787 -1.777783 0.340176 1.907981 1.581676 \n", + "\n", + " T_cont_scaled T_bin_scaled \n", + "0 0.347809 -1.022452 \n", + "1 0.726014 0.977650 \n", + "2 0.657767 0.977650 \n", + "3 1.304402 0.977650 \n", + "4 0.194070 0.977650 \n", + "... ... ... \n", + "2495 -0.475800 -1.022452 \n", + "2496 -2.779944 -1.022452 \n", + "2497 0.511847 -1.022452 \n", + "2498 -1.028702 -1.022452 \n", + "2499 1.832247 0.977650 \n", + "\n", + "[2500 rows x 23 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def inv_logit(z):\n", + " \"\"\"Compute the inverse logit (sigmoid) of z.\"\"\"\n", + " return 1 / (1 + np.exp(-z))\n", + "\n", + "\n", + "def simulate_data(n=2500, alpha_true=3.0, rho=0.6, cate_estimation=False):\n", + " # Exclusion restrictions:\n", + " # X[0], X[1] affect both Y and T (confounders)\n", + " # X[2], X[3] affect ONLY T (instruments for T)\n", + " # X[4] affects ONLY Y (predictor of Y only)\n", + "\n", + " betaY = np.array(\n", + " [0.5, -0.3, 0.0, 0.0, 0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n", + " ) # X[2], X[3] excluded\n", + " betaD = np.array(\n", + " [0.7, 0.1, -0.4, 0.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n", + " ) # X[4] excluded\n", + " p = len(betaY)\n", + "\n", + " # noise variances and correlation\n", + " sigma_U = 3.0\n", + " sigma_V = 3.0\n", + "\n", + " # design matrix (n × p) with mean-zero columns\n", + " X = np.random.normal(size=(n, p))\n", + " X = (X - X.mean(axis=0)) / X.std(axis=0)\n", + "\n", + " mean = [0, 0]\n", + " cov = [[sigma_U**2, rho * sigma_U * sigma_V], [rho * sigma_U * sigma_V, sigma_V**2]]\n", + " errors = np.random.multivariate_normal(mean, cov, size=n)\n", + " U = errors[:, 0] # error in outcome equation\n", + " V = errors[:, 1] #\n", + "\n", + " # continuous treatment\n", + " T_cont = X @ betaD + V\n", + "\n", + " # latent variable for binary treatment\n", + " T_latent = X @ betaD + V\n", + " T_bin = np.random.binomial(n=1, p=inv_logit(T_latent), size=n)\n", + "\n", + " alpha_individual = 3.0 + 2.5 * X[:, 0]\n", + "\n", + " # outcomes\n", + " Y_cont = alpha_true * T_cont + X @ betaY + U\n", + " if cate_estimation:\n", + " Y_bin = alpha_individual * T_bin + X @ betaY + U\n", + " else:\n", + " Y_bin = alpha_true * T_bin + X @ betaY + U\n", + "\n", + " # combine into DataFrame\n", + " data = pd.DataFrame(\n", + " {\n", + " \"Y_cont\": Y_cont,\n", + " \"Y_bin\": Y_bin,\n", + " \"T_cont\": T_cont,\n", + " \"T_bin\": T_bin,\n", + " }\n", + " )\n", + " data[\"alpha\"] = alpha_true + alpha_individual\n", + " for j in range(p):\n", + " data[f\"feature_{j}\"] = X[:, j]\n", + " data[\"Y_cont_scaled\"] = (data[\"Y_cont\"] - data[\"Y_cont\"].mean()) / data[\n", + " \"Y_cont\"\n", + " ].std(ddof=1)\n", + " data[\"Y_bin_scaled\"] = (data[\"Y_bin\"] - data[\"Y_bin\"].mean()) / data[\"Y_bin\"].std(\n", + " ddof=1\n", + " )\n", + " data[\"T_cont_scaled\"] = (data[\"T_cont\"] - data[\"T_cont\"].mean()) / data[\n", + " \"T_cont\"\n", + " ].std(ddof=1)\n", + " data[\"T_bin_scaled\"] = (data[\"T_bin\"] - data[\"T_bin\"].mean()) / data[\"T_bin\"].std(\n", + " ddof=1\n", + " )\n", + " return data\n", + "\n", + "\n", + "data = simulate_data()\n", + "instruments_data = data.copy()\n", + "features = [col for col in data.columns if \"feature\" in col]\n", + "formula = \"Y_cont ~ T_cont + \" + \" + \".join(features)\n", + "instruments_formula = \"T_cont ~ 1 + \" + \" + \".join(features)\n", + "data" + ] + }, + { + "cell_type": "markdown", + "id": "e2472e18", + "metadata": {}, + "source": [ + "CausalPy's `Variable Selection` module provides a way to encode our uncertainty about variable relevance directly into the prior distribution. Rather than choosing which predictors to include, we specify priors that allow coefficients to be shrunk toward zero (or exactly zero) when the data doesn't support their inclusion. The key insight is that variable selection becomes part of the inference problem rather than a preprocessing step. The module offers two fundamentally different approaches to variable selection, each reflecting a different belief about how sparsity manifests in the world.\n", + "\n", + "#### The Spike-and-Slab: Discrete Choices\n", + "\n", + "The spike-and-slab prior embodies a binary worldview: each variable either matters or it doesn't. Mathematically, we express this as:\n", + "\n", + "$$ \\beta_{j} = \\gamma_{j} \\cdot \\beta_{j_\\text{raw}}$$\n", + "\n", + "such that \n", + "\n", + "$$ \\gamma_{j} \\in \\{0, 1\\}$$\n", + "\n", + "So we have the \"spike\"—the coefficient is exactly zero. When $\\gamma_{j} = 1$, we have the \"slab\" i.e. the coefficient takes on a value from the raw distribution.\n", + "This approach appeals to our intuition about many real-world scenarios. Consider a propensity score model predicting whether someone receives a treatment. Some demographic variables might genuinely have no relationship with treatment assignment, while others are strongly predictive. The spike-and-slab says: let's let each variable clearly declare itself as relevant or irrelevant.\n", + "\n", + "#### The Regularised Horseshoe: Gentle Moderation\n", + "\n", + "The horseshoe prior takes a different philosophical stance. Instead of discrete selection, it says: effects exist on a continuum from negligible to substantial, and we should shrink them proportionally to their signal strength. Small effects get heavily shrunk (possibly to near-zero), while large effects escape shrinkage almost entirely.\n", + "\n", + "$$ \\beta_{j} = \\tau \\cdot \\tilde{\\lambda}_j \\cdot \\beta_{j\\text{raw}}$$\n", + "\n", + "where $\\tau$ is a global shrinkage parameter shared across all coefficients, and $\\tilde{\\lambda}_j$ is local or specific to each coefficient and regularised so as to ensure finite variance. \n" + ] + }, + { + "cell_type": "markdown", + "id": "806df6ea", + "metadata": {}, + "source": [ + "### Hyperparameters for Variable Selection Priors\n", + "\n", + "You can control the behaviour of the variable selection priors through some of the hyperparameters available. For the spike and slab prior, the most important hyperparamers are `temperature`, `pi_alpha`, and `pi_beta`. \n", + "\n", + "Because our sampler doesn't like discrete variables, we're approximating a bernoulli outcome in our sampling to define the spike and slab. The approximation is governed by the `temperature` parameter. The default value of 0.1 works well in most cases, creating indicators that cluster near 0 or 1 without causing sampling difficulties.\n", + "\n", + "The selection probability parameters `pi_alpha` and `pi_beta` encode your prior belief about sparsity. With both set to 2 (the default), you're placing a Beta(2,2) prior on π, the overall proportion of selected variables. This is symmetric around 0.5 but slightly concentrated there—you're saying \"I don't know how many variables are relevant, but probably not all of them and probably not none of them.\"" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "ae848fe9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 3, figsize=(20, 6))\n", + "axs = axs.flatten()\n", + "axs[0].hist(pm.draw(pm.Beta.dist(2, 2), 1000), ec=\"black\", color=\"slateblue\")\n", + "axs[1].hist(pm.draw(pm.Beta.dist(2, 5), 1000), ec=\"black\", color=\"slateblue\")\n", + "axs[2].hist(pm.draw(pm.Beta.dist(5, 2), 1000), ec=\"black\", color=\"slateblue\")\n", + "axs[1].set_title(r\"Various Distributions for the $\\pi$ hyperparameter\", size=20);" + ] + }, + { + "cell_type": "markdown", + "id": "3237bb49", + "metadata": {}, + "source": [ + "We'll now fit two models and estimate the implied treatment effect." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "763ca253", + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--------------------------------------------------------------------------------\n", + "Model 1: Normal Priors (No Variable Selection)\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:187: UserWarning: Warning. The treatment variable is not Binary.\n", + " This is not necessarily a problem but it violates\n", + " the assumption of a simple IV experiment.\n", + " The coefficients should be interpreted appropriately.\n", + " warnings.warn(\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [beta_t, beta_z, chol_cov]\n", + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b332c2837f1849329b3b561a9765f3e5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n", + " outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n", + "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n", + " outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n", + "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n", + " outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n", + "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n", + " outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n" + ] + }, + { + "data": { + "text/html": [ + "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 133 seconds.\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:187: UserWarning: Warning. The treatment variable is not Binary.\n",
+      "                This is not necessarily a problem but it violates\n",
+      "                the assumption of a simple IV experiment.\n",
+      "                The coefficients should be interpreted appropriately.\n",
+      "  warnings.warn(\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/pymc_models.py:699: UserWarning: Variable selection priors specified. The 'mus' and 'sigmas' in the priors dict will be ignored for beta coefficients. Only 'eta' and 'lkj_sd' will be used.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "Model 2: Spike-and-Slab Priors\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [pi_beta_t, beta_t_raw, gamma_beta_t_u, pi_beta_z, beta_z_raw, gamma_beta_z_u, chol_cov]\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db312f02efe743c386a4f2b4449f8904",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 551 seconds.\n",
+      "There were 167 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
+   "source": [
+    "sample_kwargs = {\n",
+    "    \"draws\": 1000,\n",
+    "    \"tune\": 2000,\n",
+    "    \"chains\": 4,\n",
+    "    \"cores\": 4,\n",
+    "    \"target_accept\": 0.95,\n",
+    "    \"progressbar\": True,\n",
+    "    \"random_seed\": 42,\n",
+    "    \"mp_ctx\": \"spawn\",\n",
+    "}\n",
+    "\n",
+    "# =========================================================================\n",
+    "# Model 1: Normal priors (no selection)\n",
+    "# =========================================================================\n",
+    "print(\"\\n\" + \"-\" * 80)\n",
+    "print(\"Model 1: Normal Priors (No Variable Selection)\")\n",
+    "print(\"-\" * 80)\n",
+    "\n",
+    "result_normal = cp.InstrumentalVariable(\n",
+    "    instruments_data=instruments_data,\n",
+    "    data=data,\n",
+    "    instruments_formula=instruments_formula,\n",
+    "    formula=formula,\n",
+    "    model=cp.pymc_models.InstrumentalVariableRegression(sample_kwargs=sample_kwargs),\n",
+    "    vs_prior_type=None,  # No variable selection\n",
+    ")\n",
+    "\n",
+    "# =========================================================================\n",
+    "# Model 2: Spike-and-Slab priors\n",
+    "# =========================================================================\n",
+    "print(\"\\n\" + \"-\" * 80)\n",
+    "print(\"Model 2: Spike-and-Slab Priors\")\n",
+    "print(\"-\" * 80)\n",
+    "\n",
+    "result_spike_slab = cp.InstrumentalVariable(\n",
+    "    instruments_data=instruments_data,\n",
+    "    data=data,\n",
+    "    instruments_formula=instruments_formula,\n",
+    "    formula=formula,\n",
+    "    model=cp.pymc_models.InstrumentalVariableRegression(sample_kwargs=sample_kwargs),\n",
+    "    vs_prior_type=\"spike_and_slab\",\n",
+    "    vs_hyperparams={\"pi_alpha\": 2, \"pi_beta\": 2, \"slab_sigma\": 2, \"temperature\": 0.1},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ccb6e0b",
+   "metadata": {},
+   "source": [
+    "The models have quite a distinct structure. Compare the normal IV model with non variable selection priors. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e97a9ca2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "clusterinstruments (15)\n",
+       "\n",
+       "instruments (15)\n",
+       "\n",
+       "\n",
+       "clustercovariates (16)\n",
+       "\n",
+       "covariates (16)\n",
+       "\n",
+       "\n",
+       "cluster3\n",
+       "\n",
+       "3\n",
+       "\n",
+       "\n",
+       "cluster2 x 2\n",
+       "\n",
+       "2 x 2\n",
+       "\n",
+       "\n",
+       "cluster2\n",
+       "\n",
+       "2\n",
+       "\n",
+       "\n",
+       "cluster2500\n",
+       "\n",
+       "2500\n",
+       "\n",
+       "\n",
+       "cluster2500 x 2\n",
+       "\n",
+       "2500 x 2\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_t\n",
+       "\n",
+       "beta_t\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_t\n",
+       "\n",
+       "mu_t\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_t->mu_t\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_z\n",
+       "\n",
+       "beta_z\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_y\n",
+       "\n",
+       "mu_y\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_z->mu_y\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov\n",
+       "\n",
+       "chol_cov\n",
+       "~\n",
+       "_LKJCholeskyCov\n",
+       "\n",
+       "\n",
+       "\n",
+       "cov\n",
+       "\n",
+       "cov\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->cov\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov_corr\n",
+       "\n",
+       "chol_cov_corr\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->chol_cov_corr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov_stds\n",
+       "\n",
+       "chol_cov_stds\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->chol_cov_stds\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "likelihood\n",
+       "\n",
+       "likelihood\n",
+       "~\n",
+       "Multivariate_normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->likelihood\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu\n",
+       "\n",
+       "mu\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_y->mu\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_t->mu\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu->likelihood\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm.model_to_graphviz(result_normal.model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34f3a1b7",
+   "metadata": {},
+   "source": [
+    "Now compare the structure of the spike and slab model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "4f8c2685",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "clusterinstruments (15)\n",
+       "\n",
+       "instruments (15)\n",
+       "\n",
+       "\n",
+       "clustercovariates (16)\n",
+       "\n",
+       "covariates (16)\n",
+       "\n",
+       "\n",
+       "cluster3\n",
+       "\n",
+       "3\n",
+       "\n",
+       "\n",
+       "cluster2 x 2\n",
+       "\n",
+       "2 x 2\n",
+       "\n",
+       "\n",
+       "cluster2\n",
+       "\n",
+       "2\n",
+       "\n",
+       "\n",
+       "cluster2500\n",
+       "\n",
+       "2500\n",
+       "\n",
+       "\n",
+       "cluster2500 x 2\n",
+       "\n",
+       "2500 x 2\n",
+       "\n",
+       "\n",
+       "\n",
+       "pi_beta_z\n",
+       "\n",
+       "pi_beta_z\n",
+       "~\n",
+       "Beta\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_z\n",
+       "\n",
+       "gamma_beta_z\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "pi_beta_z->gamma_beta_z\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "pi_beta_t\n",
+       "\n",
+       "pi_beta_t\n",
+       "~\n",
+       "Beta\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_t\n",
+       "\n",
+       "gamma_beta_t\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "pi_beta_t->gamma_beta_t\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_t_raw\n",
+       "\n",
+       "beta_t_raw\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_t\n",
+       "\n",
+       "beta_t\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_t_raw->beta_t\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_t\n",
+       "\n",
+       "mu_t\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_t->mu_t\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_t->beta_t\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_t_u\n",
+       "\n",
+       "gamma_beta_t_u\n",
+       "~\n",
+       "Uniform\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_t_u->gamma_beta_t\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_z_u\n",
+       "\n",
+       "gamma_beta_z_u\n",
+       "~\n",
+       "Uniform\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_z_u->gamma_beta_z\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_z\n",
+       "\n",
+       "beta_z\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_y\n",
+       "\n",
+       "mu_y\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_z->mu_y\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_z->beta_z\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_z_raw\n",
+       "\n",
+       "beta_z_raw\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_z_raw->beta_z\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov\n",
+       "\n",
+       "chol_cov\n",
+       "~\n",
+       "_LKJCholeskyCov\n",
+       "\n",
+       "\n",
+       "\n",
+       "cov\n",
+       "\n",
+       "cov\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->cov\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov_corr\n",
+       "\n",
+       "chol_cov_corr\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->chol_cov_corr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov_stds\n",
+       "\n",
+       "chol_cov_stds\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->chol_cov_stds\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "likelihood\n",
+       "\n",
+       "likelihood\n",
+       "~\n",
+       "Multivariate_normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->likelihood\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu\n",
+       "\n",
+       "mu\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_y->mu\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_t->mu\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu->likelihood\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm.model_to_graphviz(result_spike_slab.model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "368660c8",
+   "metadata": {},
+   "source": [
+    "Despite seeing some divergences in our spike and slab model, most other sampler health metrics seem healthy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0755095c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_energy(result_spike_slab.idata, figsize=(20, 6));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bffd8b6",
+   "metadata": {},
+   "source": [
+    "And since we know the true data generating conditions we can also assess the derived posterior treatment estimates. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "838e0726",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20, 6))\n",
+    "az.plot_posterior(\n",
+    "    result_normal.idata,\n",
+    "    var_names=[\"beta_z\"],\n",
+    "    coords={\"covariates\": [\"T_cont\"]},\n",
+    "    ax=ax,\n",
+    "    label=\"Normal\",\n",
+    ")\n",
+    "az.plot_posterior(\n",
+    "    result_spike_slab.idata,\n",
+    "    var_names=[\"beta_z\"],\n",
+    "    coords={\"covariates\": [\"T_cont\"]},\n",
+    "    ax=ax,\n",
+    "    color=\"green\",\n",
+    "    label=\"spike and slab\",\n",
+    ")\n",
+    "ax.axvline(3, color=\"black\", linestyle=\"--\", label=\"True value\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "057b4f5d",
+   "metadata": {},
+   "source": [
+    "This plot suggests that the spike and slab prior was better able to ignore noise in the process and zero in on the true effect. This will not always work but it is a sensible practice to at least sensitivity check difference between the estimates under different prior settings. We can observe how aggressively the spike and slab prior worked to cull unwanted variables from each model by comparing the values on the coefficients across each model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "127888b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axs = az.plot_forest(\n",
+    "    [result_spike_slab.idata, result_normal.idata],\n",
+    "    var_names=[\"beta_z\"],\n",
+    "    combined=True,\n",
+    "    model_names=[\"Spike and Slab\", \"Normal\"],\n",
+    "    r_hat=True,\n",
+    ")\n",
+    "axs[0].set_title(\"Parameter Comparison Outcome Model \\n Baseline v Spike and Slab\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f09b24bf",
+   "metadata": {},
+   "source": [
+    "#### The Treatment Model\n",
+    "\n",
+    "Variable selection is applied to both the outcome and the treatment model. In this way we calibrate our parameters to the joint patterns of realisations between these two endogenous variables. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "acafc928",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axs = az.plot_forest(\n",
+    "    [result_spike_slab.idata, result_normal.idata],\n",
+    "    var_names=[\"beta_t\"],\n",
+    "    combined=True,\n",
+    "    model_names=[\"Spike and Slab\", \"Normal\"],\n",
+    "    r_hat=True,\n",
+    ")\n",
+    "\n",
+    "axs[0].set_title(\"Parameter Comparison Treatment Model \\n Baseline v Spike and Slab\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07f7d95b",
+   "metadata": {},
+   "source": [
+    "The spike and slab prior can also output direct inclusion probabilities that can be used for communication regarding which variables were \"selected\" in the process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f2a0b213",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "  \n",
+       "
probselectedgamma_mean
00.01750False0.020546
11.00000True0.991285
20.64100True0.659357
30.58200True0.616580
40.17625False0.192949
50.06075False0.068297
60.66600True0.702671
70.01000False0.012679
80.01300False0.016342
90.00975False0.012392
100.01700False0.019508
110.01625False0.021217
120.02000False0.024469
130.01825False0.023474
140.02700False0.031636
150.01650False0.020863
\n",
+       "
"
+      ],
+      "text/plain": [
+       "       prob  selected  gamma_mean\n",
+       "0   0.01750     False    0.020546\n",
+       "1   1.00000      True    0.991285\n",
+       "2   0.64100      True    0.659357\n",
+       "3   0.58200      True    0.616580\n",
+       "4   0.17625     False    0.192949\n",
+       "5   0.06075     False    0.068297\n",
+       "6   0.66600      True    0.702671\n",
+       "7   0.01000     False    0.012679\n",
+       "8   0.01300     False    0.016342\n",
+       "9   0.00975     False    0.012392\n",
+       "10  0.01700     False    0.019508\n",
+       "11  0.01625     False    0.021217\n",
+       "12  0.02000     False    0.024469\n",
+       "13  0.01825     False    0.023474\n",
+       "14  0.02700     False    0.031636\n",
+       "15  0.01650     False    0.020863"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summary = result_spike_slab.model.vs_prior_outcome.get_inclusion_probabilities(\n",
+    "    result_spike_slab.idata, \"beta_z\"\n",
+    ")\n",
+    "summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38568d27",
+   "metadata": {},
+   "source": [
+    "### Horseshoe\n",
+    "\n",
+    "The horseshoe prior is a sophisticated continuous shrinkage method designed for regularization and variable selection in high-dimensional regression settings. Unlike discrete selection approaches, it operates through a elegant hierarchical structure that adaptively shrinks coefficients based on the strength of their signal in the data. The key to the implementation is the hierarchical $\\lambda$ component: \n",
+    "\n",
+    "$$ \\tilde{\\lambda}_j = \\sqrt{\\frac{c^2 \\lambda_j^2}{c^2 + \\tau^2 \\lambda_j^2}} $$\n",
+    "\n",
+    "is composed of individual local shrinkage parameters and $c^2$ is a regularization parameter that prevents over-shrinkage of genuinely large signals. \n",
+    "\n",
+    "#### The $\\tau_0$ hyperparameter\n",
+    "\n",
+    "Like the `temperature` parameter in the spike and slab model, the $\\tau_0$ parameter determines the overall level of sparsity expected in the model. However, the $tau_0$ will by default be derived from the data and the number of covariates in your data. While both the horseshoe and spike-and-slab priors address variable selection and sparsity, they embody fundamentally different philosophies about how to achieve these goals. The horseshoe embraces continuity, creating a smooth gradient of shrinkage where all coefficients remain in the model but are pulled toward zero with varying intensity. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "16bb5f90",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(20, 6))\n",
+    "axs = axs.flatten()\n",
+    "axs[0].hist(\n",
+    "    pm.draw(pm.InverseGamma.dist(2, 2), 1000) ** 2,\n",
+    "    ec=\"black\",\n",
+    "    color=\"slateblue\",\n",
+    "    bins=30,\n",
+    ")\n",
+    "axs[1].hist(\n",
+    "    pm.draw(pm.InverseGamma.dist(3, 3), 1000) ** 2,\n",
+    "    ec=\"black\",\n",
+    "    color=\"slateblue\",\n",
+    "    bins=30,\n",
+    ")\n",
+    "axs[2].hist(\n",
+    "    pm.draw(pm.InverseGamma.dist(4, 4), 1000) ** 2,\n",
+    "    ec=\"black\",\n",
+    "    color=\"slateblue\",\n",
+    "    bins=30,\n",
+    ")\n",
+    "axs[1].set_title(r\"Various Distributions for the $c^{2}$ hyperparameter\", size=20);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "63edfa4e",
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "Model 3: Horseshoe Priors\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:187: UserWarning: Warning. The treatment variable is not Binary.\n",
+      "                This is not necessarily a problem but it violates\n",
+      "                the assumption of a simple IV experiment.\n",
+      "                The coefficients should be interpreted appropriately.\n",
+      "  warnings.warn(\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/pymc_models.py:699: UserWarning: Variable selection priors specified. The 'mus' and 'sigmas' in the priors dict will be ignored for beta coefficients. Only 'eta' and 'lkj_sd' will be used.\n",
+      "  warnings.warn(\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [tau_beta_t, lambda_beta_t, c2_beta_t, beta_t_raw, tau_beta_z, lambda_beta_z, c2_beta_z, beta_z_raw, chol_cov]\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1410b52c995348e397aed1a2843f2bb7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 534 seconds.\n",
+      "There were 16 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
+   "source": [
+    "# =========================================================================\n",
+    "# Model 2: Horseshoe priors\n",
+    "# =========================================================================\n",
+    "print(\"\\n\" + \"-\" * 80)\n",
+    "print(\"Model 3: Horseshoe Priors\")\n",
+    "print(\"-\" * 80)\n",
+    "\n",
+    "result_horseshoe = cp.InstrumentalVariable(\n",
+    "    instruments_data=instruments_data,\n",
+    "    data=data,\n",
+    "    instruments_formula=instruments_formula,\n",
+    "    formula=formula,\n",
+    "    model=cp.pymc_models.InstrumentalVariableRegression(sample_kwargs=sample_kwargs),\n",
+    "    vs_prior_type=\"horseshoe\",\n",
+    "    vs_hyperparams={\"c2_alpha\": 3, \"c2_beta\": 3},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db7d86aa",
+   "metadata": {},
+   "source": [
+    "Similar to the inclusion probabilities in the spike and slab model, a horseshoe model can output the relative shrinkage factor that gets applied to each variables inclusion. This method of variable is less decisive than spike and slab, but also mitigates case of completely zero-ing the small but real contributions of certain variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "9c283ee1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "
shrinkage_factorlambda_tildetau
00.0147801.2225240.036811
10.69773957.7139610.036811
20.16028213.2577980.036811
30.12964910.7240230.036811
40.0766766.3422980.036811
50.0256502.1216800.036811
60.19018615.7313980.036811
70.0141981.1743820.036811
80.0146361.2106340.036811
90.0140241.1600020.036811
100.0144411.1944850.036811
110.0189071.5639340.036811
120.0185181.5317340.036811
130.0174151.4405140.036811
140.0192381.5912910.036811
150.0174811.4459460.036811
\n",
+       "
"
+      ],
+      "text/plain": [
+       "    shrinkage_factor  lambda_tilde       tau\n",
+       "0           0.014780      1.222524  0.036811\n",
+       "1           0.697739     57.713961  0.036811\n",
+       "2           0.160282     13.257798  0.036811\n",
+       "3           0.129649     10.724023  0.036811\n",
+       "4           0.076676      6.342298  0.036811\n",
+       "5           0.025650      2.121680  0.036811\n",
+       "6           0.190186     15.731398  0.036811\n",
+       "7           0.014198      1.174382  0.036811\n",
+       "8           0.014636      1.210634  0.036811\n",
+       "9           0.014024      1.160002  0.036811\n",
+       "10          0.014441      1.194485  0.036811\n",
+       "11          0.018907      1.563934  0.036811\n",
+       "12          0.018518      1.531734  0.036811\n",
+       "13          0.017415      1.440514  0.036811\n",
+       "14          0.019238      1.591291  0.036811\n",
+       "15          0.017481      1.445946  0.036811"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summary = result_horseshoe.model.vs_prior_outcome.get_shrinkage_factors(\n",
+    "    result_horseshoe.idata, \"beta_z\"\n",
+    ")\n",
+    "summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "82b0121c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20, 6))\n",
+    "az.plot_posterior(\n",
+    "    result_normal.idata, var_names=[\"beta_z\"], coords={\"covariates\": [\"T_cont\"]}, ax=ax\n",
+    ")\n",
+    "az.plot_posterior(\n",
+    "    result_horseshoe.idata,\n",
+    "    var_names=[\"beta_z\"],\n",
+    "    coords={\"covariates\": [\"T_cont\"]},\n",
+    "    ax=ax,\n",
+    "    color=\"green\",\n",
+    ")\n",
+    "ax.axvline(3, color=\"black\", linestyle=\"--\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e15d4f1e",
+   "metadata": {},
+   "source": [
+    "In this case it seems the horseshoe prior leads a bi-modal posterior estimate of the treatment effect suggesting a kind of indecision about the level of sparsity to apply. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc265f5d",
+   "metadata": {},
+   "source": [
+    "### Binary Treatment Case\n",
+    "\n",
+    "Our data generating function output two different simulation scenarios, where the treatment was either continuous or binary. This allows us to demonstrate the joint modelling of the binary treatment outcome which uses a Bernoulli likelihood for the treatment variable and latent confounding to model the joint realisation of treatment and outcome. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "89e61d28",
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "Model 1: Normal Priors Binary Treatment (No Variable Selection)\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [beta_t, beta_z, sigma_U, rho_unconstr, eps_raw]\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7276bf767824813a312c4c3ccff7c01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 68 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
+   "source": [
+    "formula = \"Y_bin ~ T_bin + \" + \" + \".join(features)\n",
+    "instruments_formula = \"T_bin ~ 1 + \" + \" + \".join(features)\n",
+    "\n",
+    "\n",
+    "# =========================================================================\n",
+    "# Model 1: Normal priors (no selection)\n",
+    "# =========================================================================\n",
+    "print(\"\\n\" + \"-\" * 80)\n",
+    "print(\"Model 1: Normal Priors Binary Treatment (No Variable Selection)\")\n",
+    "print(\"-\" * 80)\n",
+    "\n",
+    "result_normal_binary = cp.InstrumentalVariable(\n",
+    "    instruments_data=instruments_data,\n",
+    "    data=data,\n",
+    "    instruments_formula=instruments_formula,\n",
+    "    formula=formula,\n",
+    "    model=cp.pymc_models.InstrumentalVariableRegression(sample_kwargs=sample_kwargs),\n",
+    "    vs_prior_type=None,  # No variable selection\n",
+    "    binary_treatment=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "98c1b50a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20, 6))\n",
+    "az.plot_posterior(\n",
+    "    result_normal.idata, var_names=[\"beta_z\"], coords={\"covariates\": [\"T_cont\"]}, ax=ax\n",
+    ")\n",
+    "az.plot_posterior(\n",
+    "    result_normal_binary.idata,\n",
+    "    var_names=[\"beta_z\"],\n",
+    "    coords={\"covariates\": [\"T_bin\"]},\n",
+    "    ax=ax,\n",
+    "    color=\"green\",\n",
+    ")\n",
+    "ax.axvline(3, color=\"black\", linestyle=\"--\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36ad018b",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Conclusion: Choosing Your Path Through Uncertainty\n",
+    "\n",
+    "Variable selection priors offer a principled way to navigate the tension between model complexity and causal identification. Rather than forcing binary decisions about which variables to include, these priors encode our uncertainty about variable relevance directly into the inferential framework. But as we've seen, the choice between spike-and-slab and horseshoe reflects deeper commitments about how sparsity manifests in the world.\n",
+    "\n",
+    "**The spike-and-slab prior** embodies decisiveness. It asks: which variables truly matter? By pushing coefficients toward exactly zero or allowing them to take on substantial values, it produces interpretable inclusion probabilities that clearly communicate which predictors the model has \"selected.\" This approach shines when you believe that many potential confounders are genuine noise—included out of caution but ultimately irrelevant to the causal mechanism. The discrete nature of selection also makes results easier to communicate to stakeholders who think in terms of \"what factors matter?\" \n",
+    "\n",
+    "**The horseshoe prior** embraces nuance. It acknowledges that effects exist on a continuum, and that small but real contributions shouldn't be entirely zeroed out. The continuous shrinkage allows weak signals to persist (heavily damped) while strong signals emerge largely unscathed. This is valuable when you suspect that multiple confounders have genuine but varying degrees of influence, and when premature exclusion of any single variable might introduce bias. The regularization parameter $c^2$ acts as a safeguard, preventing even the horseshoe's aggressive shrinkage from overwhelming genuinely large effects.\n",
+    " \n",
+    "In our simulations, both approaches identified the true treatment effect of 3, though they arrived there differently. The spike-and-slab showed more confidence, producing tighter posterior intervals by decisively excluding noise variables. The horseshoe's bi-modal posterior in some specifications revealed its uncertainty about the appropriate level of sparsity a kind of probabilistic humility that spike-and-slab's discrete choices don't allow.\n",
+    "\n",
+    "**Practical Guidance:**\n",
+    " \n",
+    "- **Use spike-and-slab when** you have strong priors about sparsity (many potential confounders, few true ones), when interpretability matters (stakeholders want to know \"what's included?\"), or when you're willing to trade some flexibility for more decisive inference.\n",
+    " \n",
+    "- **Use horseshoe when** you're uncertain about sparsity levels, when small effects might still matter for causal identification, or when you want the model to smoothly adapt its shrinkage to the data without hard inclusion/exclusion decisions.\n",
+    " \n",
+    "- **Use neither when** theory clearly identifies your confounders, when sample size is large relative to the number of predictors, or when the cost of Type I errors (including irrelevant variables) is low relative to Type II errors (excluding true confounders).\n",
+    " \n",
+    "**Final Thoughts:**\n",
+    " \n",
+    "Variable selection priors don't eliminate the need for causal reasoning. They don't tell you which variables are *causally* relevant, only which are *statistically* predictive. But when used thoughtfully—guided by theory about potential confounders, informed by domain knowledge about likely sparsity patterns, and validated through sensitivity analysis. They offer a middle path between the Scylla of over-specification (including everything) and the Charybdis of under-specification (excluding too much). Used within a joint model of treatment and outcome variable, the argument of a variable selection routine  represents an attempt to calibrate the parameters to select the instrument structure. What variable selection is really doing in joint treatment-outcome models is calibrating the parameters to discover patterns consistent with instrument structure *if such structure exists in the data*. The horseshoe shrinks away coefficients that appear redundant given the covariance structure between treatment, outcome, and covariates. The spike-and-slab actively excludes variables that don't contribute to explaining either margin after accounting for shared variation.\n",
+    " \n",
+    "The ideal use of variable selection in instrumental variable designs is not as a replacement for domain knowledge but as a consistency check. The real power of these methods lies not in automation but in transparency. By making variable selection part of the posterior distribution rather than a pre-processing step, we can quantify and communicate our uncertainty about model structure itself. This moves us closer to the goal of all principled causal inference: not just estimating effects, but understanding the limits of what we can learn from the data we have.\n",
+    " \n",
+    "As always in causal inference, the model is a question posed to the data. Variable selection priors help us ask that question more precisely, but we still need theory to tell us if we're asking the right question at all.\n",
+    "\n"
+   ]
+  }
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