causalis.scenarios.multi_unconfoundedness.refutation.overlap.overlap_plot

Module Contents

Functions

plot_m_overlap	Multi-treatment overlap plot for pairwise conditional propensity scores.
overlap_plot	Convenience wrapper to match overlap_plot(data, estimate) API style.

Data

__all__

API

causalis.scenarios.multi_unconfoundedness.refutation.overlap.overlap_plot.plot_m_overlap(diag: Union[causalis.data_contracts.causal_diagnostic_data.MultiUnconfoundednessDiagnosticData, causalis.data_contracts.multicausal_estimate.MultiCausalEstimate, dict, Any], clip: Tuple[float, float] = (0.01, 0.99), bins: Any = 'fd', kde: bool = True, shade_overlap: bool = True, ax: Optional[matplotlib.pyplot.Axes] = None, figsize: Tuple[float, float] = (9, 5.5), dpi: int = 220, font_scale: float = 1.15, save: Optional[str] = None, save_dpi: Optional[int] = None, transparent: bool = False, color_t: Optional[Any] = None, color_c: Optional[Any] = None, *, treatment_idx: Optional[Union[int, List[int]]] = None, baseline_idx: int = 0, treatment_names: Optional[List[str]] = None) → matplotlib.pyplot.Figure

Multi-treatment overlap plot for pairwise conditional propensity scores.

For each comparison baseline (default 0) vs k, this plots P(D=k | X, D in {baseline, k}) = m_k(X) / (m_baseline(X) + m_k(X)) on the observed pair sample D in {baseline, k}, comparing:

• units with D=k (treated for the pair),

• units with D=baseline (control for the pair).

Parameters:

• diag.d: (n, K) one-hot

• diag.m_hat / diag.m_hat_raw: (n, K) propensity

• treatment_idx:

  • None -> plot all k != baseline_idx (multi-panel)

  • int -> plot one comparison

  • list[int] -> plot selected comparisons

• ax: supported only for a single comparison (exactly one k)

Returns matplotlib.figure.Figure.

causalis.scenarios.multi_unconfoundedness.refutation.overlap.overlap_plot.overlap_plot(data: causalis.data_contracts.multicausaldata.MultiCausalData, estimate: causalis.data_contracts.multicausal_estimate.MultiCausalEstimate, **kwargs: Any) → matplotlib.pyplot.Figure

Convenience wrapper to match overlap_plot(data, estimate) API style.

causalis.scenarios.multi_unconfoundedness.refutation.overlap.overlap_plot.__all__

[‘plot_m_overlap’, ‘overlap_plot’]