causalis.scenarios.unconfoundedness.refutation.unconfoundedness.love_plot

Love plot for covariate balance before and after weighting.

Module Contents

Functions

love_plot

Plot covariate balance before and after weighting implied by an estimate.

Data

__all__

API

causalis.scenarios.unconfoundedness.refutation.unconfoundedness.love_plot.love_plot(data: causalis.dgp.causaldata.CausalData, estimate: causalis.data_contracts.causal_estimate.CausalEstimate, *, threshold: float = 0.1, 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 covariate balance before and after weighting implied by an estimate.

Parameters

data : CausalData Dataset used to fit the estimator. estimate : CausalEstimate Effect estimate with diagnostic data needed for balance diagnostics. threshold : float, default 0.10 Reference threshold for absolute standardized mean differences. figsize : tuple, optional Figure size. Defaults to an auto-scaled height based on confounder count. 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.unconfoundedness.love_plot.__all__

[‘love_plot’]