An Efficient Doubly-Robust Test for the Kernel Treatment Effect

2023/04/26 by Diego Martinez-Taboada, Aaditya Ramdas, Martinez-Taboada, Diego +3 · 4 citations
Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Machine Learning (stat.ML) #Methodology (stat.ME) #Statistical Methods and Inference #Statistical Methods in Clinical Trials

paper · pdf · doi:10.48550/arxiv.2304.13237

openalex publication_date 2023/04/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The average treatment effect, which is the difference in expectation of the counterfactuals, is probably the most popular target effect in causal inference with binary treatments. However, treatments may have effects beyond the mean, for instance decreasing or increasing the variance. We propose a new kernel-based test for distributional effects of the treatment. It is, to the best of our knowledge, the first kernel-based, doubly-robust test with provably valid type-I error. Furthermore, our proposed algorithm is computationally efficient, avoiding the use of permutations.

Cited by

• Kernel Treatment Effects with Adaptively Collected Data
• GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomes
• DoubleGen: Debiased Generative Modeling of Counterfactuals
• A Two-armed Bandit Framework for A/B Testing

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