Identification and estimation of average causal effects when treatment status is ignorable within unobserved strata
扩展了匹配和倾向得分加权方法，处理未观测变量同时影响处理分配和反事实结果的情况，通过潜类模型分配个体到未观测层，再比较同层内处理与未处理个体的结果来识别平均因果效应，并应用于估计帮派成员身份对暴力犯罪的影响。
This paper extends matching and propensity-score reweighting methods to settings in which unobserved variables influence both treatment assignment and counterfactual outcomes. Identification proceeds under the assumption that counterfactual outcomes are independent of treatment status conditional on observed covariates and membership in one of a finite set of latent classes. Individuals are first assigned to latent classes according to posterior probabilities of class membership derived from a finite-mixture model that relates a set of auxiliary variables to latent class membership. Average causal effects are then identified by comparing outcomes among treated and untreated individuals assigned to the same class, correcting for misclassifications arising in the first step. The identification procedure suggests computationally attractive latent-class matching and propensity-score reweighting estimators that obviate the need to directly estimate the distributions of counterfactual outcomes. In Monte Carlo studies, the resulting estimates are centered around the correct average causal effects with minimal loss of precision compared to competing estimators that misstate those effects. I apply the methods to estimate the effect of gang membership on violent delinquency.