causalkit.eda.eda.CausalEDA.confounders_roc_auc

CausalEDA.confounders_roc_auc(ps=None)

Compute ROC AUC of treatment assignment vs. estimated propensity score.

Interpretation: Higher AUC means treatment is more predictable from confounders, indicating stronger systematic differences between groups (potential confounding). Values near 0.5 suggest random-like assignment.

Return type:

float

Parameters:

ps (ndarray | None)