causalis.scenarios.unconfoundedness.refutation.overlap.feature_importance_plot

Native feature-importance plots for IRM nuisance learners.

Module Contents

Functions

plot_feature_importance

Plot native feature importances collected from IRM nuisance learners.

Data

__all__

API

causalis.scenarios.unconfoundedness.refutation.overlap.feature_importance_plot.plot_feature_importance(estimate: causalis.data_contracts.causal_estimate.CausalEstimate, *, top_k: int = 20, figsize: Optional[Tuple[float, float]] = None, dpi: int = 220, font_scale: float = 1.1, save: Optional[str] = None, save_dpi: Optional[int] = None, transparent: bool = False) → matplotlib.pyplot.Figure

Plot native feature importances collected from IRM nuisance learners.

Parameters

estimate : CausalEstimate Effect estimate with diagnostic_data.feature_importance collected by fitting IRM with store_diagnostics=True. top_k : int, default 20 Number of top features to show per nuisance learner. figsize : tuple, optional Figure size. Defaults to an auto-scaled height based on top_k. dpi : int, default 220 Dots per inch. font_scale : float, default 1.10 Font scaling factor. save : str, optional Path to save the figure. save_dpi : int, optional DPI for saving. transparent : bool, default False Whether to save with transparency.

Returns

matplotlib.figure.Figure The generated figure.

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

[‘plot_feature_importance’]