Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing

• URL: http://arxiv.org/abs/2603.16829v1
• Date: Tue, 17 Mar 2026 17:35:32 GMT
• Title: Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing
• Abstract summary: We propose a novel estimand to capture conditional distributional treatment effects.<n>We develop a test for the global homogeneity of conditional potential outcome distributions.<n>We derive exact closed-form expressions for two natural discrepancies.
• Score: 2.2566128651384454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Beyond conditional average treatment effects, treatments may impact the entire outcome distribution in covariate-dependent ways, for example, by altering the variance or tail risks for specific subpopulations. We propose a novel estimand to capture such conditional distributional treatment effects, and develop a doubly robust estimator that is minimax optimal in the local asymptotic sense. Using this, we develop a test for the global homogeneity of conditional potential outcome distributions that accommodates discrepancies beyond the maximum mean discrepancy (MMD), has provably valid type 1 error, and is consistent against fixed alternatives -- the first test, to our knowledge, with such guarantees in this setting. Furthermore, we derive exact closed-form expressions for two natural discrepancies (including the MMD), and provide a computationally efficient, permutation-free algorithm for our test.

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