Add variable selection priors #568
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Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
| :param 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. | ||
| :param vs_hyperparams : dict, optional |
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This is sphinx format and not numpy
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We should add that into AGENTS.md if it's not already there.
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This is sphinx format and not numpy
You got me. The doc strings were AI generated. Will fix.
| Provides continuous shrinkage with heavy tails, allowing strong signals | ||
| to escape shrinkage while weak signals are dampened: | ||
| β_j = τ · λ̃_j · β_j^raw |
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We should be able to add maths in here for nice rendering in the API docs
Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
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Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
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Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
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Marking this one as ready for review. There is still some work to be done on the notebook illustrating the functionality. But i think there is enough here that's it worth flagging the architecture choices for discussion. I've made the variable selection priors available as a module. Currently, just integrated with the IV class, but in principle can be dropped into all regression based modules with coefficients. The pattern simply requires an if-else block to be used in e.g. the propensity score model, linear regression model etc.... What do you guys think? |
Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>
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Great @NathanielF ! I think the notebooks need a bit more storyline and explanation 🙏 |
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Cool, thanks @juanitorduz . I can take another pass at it this weekend. |
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Will take a look at this. But it's worth updating from |
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It could be worth adding a graphviz DAG here to represent the DGP for the simulated data.
I'd also be tempted to add a corresponding short paragraph to explain the data setup a bit.
Could add in a link to the knowledge base page you recently added
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Can we add bibtex references (and citations in text) for the spike-and-slab and the horseshoe.
For the horseshoe, are the lambdas also {0,1} ?
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temperature and pi could more clearly map on to the equations given in the above spike-and-slab + horseshoe sections
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Can you add a bit of explanation. I'm still not over my cold, so I'm probably missing something, but my naive prediction would be that parameters would be heaped at zero. Could you just add something in there to address whatever misunderstanding I have there
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Added some comments on the new notebook. Not looked at the code yet. |
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Just a draft for the minute. Working through some ideas.
📚 Documentation preview 📚: https://causalpy--568.org.readthedocs.build/en/568/