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 efficient, avoiding the use of permutations.

翻译：平均处理效应是反事实期望的差值，可能是二元处理因果推断中最常用的目标效应。然而，处理可能产生超出均值的影响，例如减少或增加方差。我们针对处理的分布效应提出了一种新的基于核的检验。据我们所知，这是首个基于核的、具有可证伪的I类错误控制的双重稳健检验。此外，我们提出的算法高效，避免了重排列的使用。