causalis.scenarios.multi_unconfoundedness.refutation.score.residual_plots

Residual diagnostic plots for multi-treatment nuisance models g_k and m_k.

Module Contents

Functions

plot_residual_diagnostics

Plot residual diagnostics for multi-treatment nuisance models.

Data

__all__

API

causalis.scenarios.multi_unconfoundedness.refutation.score.residual_plots.plot_residual_diagnostics(estimate: causalis.data_contracts.multicausal_estimate.MultiCausalEstimate, data: Optional[causalis.data_contracts.multicausaldata.MultiCausalData] = None, *, clip_propensity: float = 1e-06, n_bins: int = 20, marker_size: float = 12.0, alpha: float = 0.35, figsize: Tuple[float, float] = (14.0, 4.8), dpi: int = 220, font_scale: float = 1.1, save: Optional[str] = None, save_dpi: Optional[int] = None, transparent: bool = False) → matplotlib.pyplot.Figure

Plot residual diagnostics for multi-treatment nuisance models.

Panels

1..K. Arm-specific residual-vs-fitted: u_k = y - g_k vs g_k within arm D_k=1. K+1. Binned calibration error for each arm: E[D_k - m_k | m_k in bin] vs binned m_k.

causalis.scenarios.multi_unconfoundedness.refutation.score.residual_plots.__all__

[‘plot_residual_diagnostics’]