WELFARE ANALYSIS VIA MARGINAL TREATMENT EFFECTS
证明在存在内生性的因果结构中，平均社会福利函数可通过边际处理效应识别并表示，为处理选择的统计决策规则（如经验福利最大化）提供了理论基础，并给出了最坏情况平均福利损失的收敛速度。
We consider a causal structure with endogeneity, i.e., unobserved confoundedness, where an instrumental variable is available. In this setting, we show that the mean social welfare function can be identified and represented via the marginal treatment effect as the operator kernel. This representation result can be applied to a variety of statistical decision rules for treatment choice, including plug-in rules, Bayes rules, and empirical welfare maximization rules. Focusing on the application of the empirical welfare maximization framework, we provide convergence rates of the worst-case average welfare loss (regret).