causalis.scenarios.cuped.diagnostics.forest_plot

Module Contents

Functions

cuped_forest_plot

Forest plot of absolute estimates and CIs for CUPED vs non-CUPED.

API

causalis.scenarios.cuped.diagnostics.forest_plot.cuped_forest_plot(estimate_with_cuped: causalis.data_contracts.causal_estimate.CausalEstimate, estimate_without_cuped: Optional[causalis.data_contracts.causal_estimate.CausalEstimate] = None, ax: Optional[matplotlib.pyplot.Axes] = None, figsize: Tuple[float, float] = (8.5, 3.8), dpi: int = 220, font_scale: float = 1.1, label_with_cuped: str = 'With CUPED', label_without_cuped: str = 'Without CUPED', color_with_cuped: str = 'C0', color_without_cuped: str = 'C1', save: Optional[str] = None, save_dpi: Optional[int] = None, transparent: bool = False) → matplotlib.pyplot.Figure

Forest plot of absolute estimates and CIs for CUPED vs non-CUPED.

Parameters

estimate_with_cuped : CausalEstimate Effect estimated with CUPED adjustment. estimate_without_cuped : CausalEstimate, optional Effect estimated without CUPED adjustment. If omitted, the function uses estimate_with_cuped.diagnostic_data.ate_naive and estimate_with_cuped.diagnostic_data.se_naive to build a normal-approx CI.