Partial identification and inference for conditional distributions of treatment effects
研究在给定可观测协变量条件下，处理效应条件分布的识别与推断问题。通过Makarov界获得分布边界，并针对内生处理问题提出随机占优假设以收紧边界，为非参数估计建立了统一有效的渐近理论。
Summary This paper considers identification and inference for the distribution of treatment effects conditional on observable covariates. Since the conditional distribution of treatment effects is not point identified without strong assumptions, we obtain bounds on the conditional distribution of treatment effects by using the Makarov bounds. We also consider the case where the treatment is endogenous and propose two stochastic dominance assumptions to tighten the bounds. We develop a nonparametric framework to estimate the bounds and establish the asymptotic theory that is uniformly valid over the support of treatment effects. An empirical example illustrates the usefulness of the methods.