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.

翻译：平均处理效应（即反事实期望之差）可能是二值处理因果推断中最常用的目标效应。然而，处理变量可能产生超越均值的影响，例如减小或增大方差。针对处理的分布效应，我们提出一种基于核的新检验方法。据我们所知，这是首个具有可证明第一类错误控制能力的核方法双重稳健检验。此外，所提算法计算高效，无需使用置换检验。