# `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’]