arXiv Machine Learning
Sep 23

Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing

The paper introduces a new estimand for conditional distributional treatment effects that captures how treatments influence the entire outcome distribution, including variance and tail risks, in a covariate-dependent manner. It presents a doubly robust estimator that is minimax optimal locally and uses it to construct a test for global homogeneity of conditional potential outcome distributions. The test accommodates discrepancies beyond the maximum mean discrepancy, guarantees valid type‑1 error, is consistent against fixed alternatives, and includes a computationally efficient, permutation‑free algorithm with exact closed‑form expressions for two natural discrepancies.

By Saksham Jain, Alex Luedtke