Nonparametric identification and estimation of heterogeneous causal effects under conditional independence
提出一种非参数方法，在条件独立假设下识别异质性因果效应的分布，通过扩展统计反卷积方法到鲁宾因果框架，并用蒙特卡洛实验和失业工人工资损失案例验证了方法的稳健性。
In this article, I propose a nonparametric strategy to identify the distribution of heterogeneous causal effects. A set of identification restrictions proposed in this article differs from existing approaches in three ways. First, it extends the random coefficient model by allowing potentially nonlinear interactions between distributional parameters and the set of covariates. Second, the causal effect distributions identified in this article give an alternative to those under the rank invariance assumption. Third, identified distribution lies within the sharp bound of distributions of the treatment effect. I develop a consistent nonparametric estimator exploiting the identifying restriction by extending the conventional statistical deconvolution method to the Rubin causal framework. Results from a Monte Carlo experiment and an application to wage loss of displaced workers suggest that the method yields robust estimates under various scenarios.