研究治疗对特定人群分布的影响，提出高效且可靠的统计方法。
Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing
- 提出新指标捕捉治疗对结果分布的条件影响。
- 开发双重稳健估计器，实现最小最大最优性。
- 首个在分布同质性检验中保证错误率与一致性的方法。
除了条件平均处理效应外，治疗可能以协变量相关的方式影响结果的整体分布，例如改变特定子群体的方差或尾部风险。本文提出一种新的估计量来捕捉此类条件分布处理效应，并开发出一种双重稳健估计器，在局部渐近意义下达到最小最大最优。基于此，我们构建了一种全局同质性检验方法，可容纳超出最大均值差异（MMD）的偏差，具有可证明的严格第一类错误控制，且对固定替代假设具有一致性——据我们所知，这是该领域首个具备此类保证的检验方法。此外，我们推导出两种自然差异度量（包括MMD）的精确闭式表达式，并提供一种计算高效、无需置换的算法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。