InversePropensityWeighting.plot_balance_ecdf#

InversePropensityWeighting.plot_balance_ecdf(covariate, idata=None, weighting_scheme=None)[source]#

Plot the empirical CDF of a covariate before and after IPW adjustment.

Produces a two-panel figure comparing the raw (unweighted) ECDFs of the treated and control groups with the reweighted ECDFs. This serves as a visual balance diagnostic: well-balanced covariates should show overlapping ECDFs in the right-hand panel.

Parameters:

  • covariate (str) – Name of the covariate column (must be one of the model’s design matrix labels) to check for balance.

  • idata (InferenceData | None) – ArviZ InferenceData containing posterior propensity score samples. If None, uses self.model.idata.

  • weighting_scheme (str | None) – Weighting scheme to apply. One of 'raw', 'robust', or 'overlap'. If None, falls back to self.weighting_scheme.

Returns:

The matplotlib Figure and a list of two Axes objects (raw ECDF on the left, weighted ECDF on the right).

Return type:

tuple[plt.Figure, list[plt.Axes]]