add docstring for class DoubleMLAPOS

Author: JanTeichertKluge

doubleml/irm/apos.py

@@ -21,7 +21,69 @@
 
 
 class DoubleMLAPOS(SampleSplittingMixin):
-    """Double machine learning for interactive regression models with multiple discrete treatments."""
+    """Double machine learning for interactive regression models with multiple discrete
+    treatments.
+
+    Parameters
+    ----------
+    obj_dml_data : :class:`DoubleMLData` object
+        The :class:`DoubleMLData` object providing the data and specifying the variables for the causal model.
+
+    ml_g : estimator implementing ``fit()`` and ``predict()``
+        A machine learner implementing ``fit()`` and ``predict()`` methods (e.g.
+        :py:class:`sklearn.ensemble.RandomForestRegressor`) for the nuisance function :math:`g_0(D, X) = E[Y | X, D]`.
+        For a binary outcome variable :math:`Y` (with values 0 and 1), a classifier implementing ``fit()`` and
+        ``predict_proba()`` can also be specified. If :py:func:`sklearn.base.is_classifier` returns ``True``,
+        ``predict_proba()`` is used otherwise ``predict()``.
+
+    ml_m : classifier implementing ``fit()`` and ``predict_proba()``
+        A machine learner implementing ``fit()`` and ``predict_proba()`` methods (e.g.
+        :py:class:`sklearn.ensemble.RandomForestClassifier`) for the nuisance function :math:`m_0(X) = E[D | X]`.
+
+    treatment_levels : iterable of int or float
+        The treatment levels for which average potential outcomes are evaluated. Each element must be present in the
+        treatment variable ``d`` of ``obj_dml_data``.
+
+    n_folds : int
+        Number of folds.
+        Default is ``5``.
+
+    n_rep : int
+        Number of repetitions for the sample splitting.
+        Default is ``1``.
+
+    score : str
+        A str (``'APO'``) specifying the score function.
+        Default is ``'APO'``.
+
+    weights : array, dict or None
+        A numpy array of weights for each individual observation. If ``None``, then the ``'APO'`` score
+        is applied (corresponds to weights equal to 1).
+        An array has to be of shape ``(n,)``, where ``n`` is the number of observations.
+        A dictionary can be used to specify weights which depend on the treatment variable.
+        In this case, the dictionary has to contain two keys ``weights`` and ``weights_bar``, where the values
+        have to be arrays of shape ``(n,)`` and ``(n, n_rep)``.
+        Default is ``None``.
+
+    normalize_ipw : bool
+        Indicates whether the inverse probability weights are normalized.
+        Default is ``False``.
+
+    trimming_rule : str, optional, deprecated
+        (DEPRECATED) A str (``'truncate'`` is the only choice) specifying the trimming approach.
+        Use ``ps_processor_config`` instead. Will be removed in a future version.
+
+    trimming_threshold : float, optional, deprecated
+        (DEPRECATED) The threshold used for trimming.
+        Use ``ps_processor_config`` instead. Will be removed in a future version.
+
+    ps_processor_config : PSProcessorConfig, optional
+        Configuration for propensity score processing (clipping, calibration, etc.).
+
+    draw_sample_splitting : bool
+        Indicates whether the sample splitting should be drawn during initialization of the object.
+        Default is ``True``.
+    """
 
     def __init__(
         self,