causalis.scenarios.unconfoundedness.refutation.overlap.overlap_plot API Reference | Causalis

function

causalis.scenarios.unconfoundedness.refutation.overlap.overlap_plot.plot_m_overlap

plot_m_overlap

Overlap plot for m(x)=P(D=1|X) with high-res rendering.

x in [0,1]
Stable NumPy KDE w/ boundary reflection (no SciPy warnings)
Uses Matplotlib default colors unless color_t/color_c are provided

Parameters

diagUnconfoundednessDiagnosticData or CausalEstimate: Diagnostic data directly, or an estimate containing diagnostic_data with m_hat and d.
cliptuple, default (0.01, 0.99): Quantiles to clip for KDE range.
binsstr or int, default “fd”: Histogram bins.
kdebool, default True: Whether to show KDE.
shade_overlapbool, default True: Whether to shade the overlap area.
axmatplotlib.axes.Axes, optional: Existing axes to plot on.
figsizetuple, default (9, 5.5): Figure size.
dpiint, default 220: Dots per inch.
font_scalefloat, default 1.15: Font scaling factor.
savestr, optional: Path to save the figure.
save_dpiint, optional: DPI for saving.
transparentbool, default False: Whether to save with transparency.
color_tcolor, optional: Color for treated group.
color_ccolor, optional: Color for control group.

Returns

matplotlib.figure.Figure

The generated figure.

Notes

This figure compares the treated and control distributions of $m(X) = \mathbb{P}(D=1 \mid X)$ . Good overlap means both groups place noticeable mass in the same regions of the unit interval. Clear separation or strong piling-up near 0 and 1 is a warning sign for unstable weighting.

Examples

Canonical target

causalis.scenarios.unconfoundedness.refutation.overlap.overlap_plot.plot_m_overlap

Sections

ParametersReturnsNotesExamples

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