feature_importance_plot

Submodule causalis.scenarios.unconfoundedness.refutation.overlap.feature_importance_plot with no child pages and 2 documented members.

Functions

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Data

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function

causalis.scenarios.unconfoundedness.refutation.overlap.feature_importance_plot.plot_feature_importance

plot_feature_importance

Plot native feature importances collected from IRM nuisance learners.

Parameters

estimateCausalEstimate: Effect estimate with diagnostic_data.feature_importance collected by fitting IRM with store_diagnostics=True.
top_kint, default 20: Number of top features to show per nuisance learner.
figsizetuple, optional: Figure size. Defaults to an auto-scaled height based on top_k.
dpiint, default 220: Dots per inch.
font_scalefloat, default 1.10: Font scaling factor.
savestr, optional: Path to save the figure.
save_dpiint, optional: DPI for saving.
transparentbool, default False: Whether to save with transparency.

Returns

matplotlib.figure.Figure

The generated figure.

Canonical target

causalis.scenarios.unconfoundedness.refutation.overlap.feature_importance_plot.plot_feature_importance

Sections

ParametersReturns

data

causalis.scenarios.unconfoundedness.refutation.overlap.feature_importance_plot.__all__

Value: ['plot_feature_importance']

[‘plot_feature_importance’]

Canonical target

causalis.scenarios.unconfoundedness.refutation.overlap.feature_importance_plot.__all__