Instrumental Variable Estimation of Dynamic Treatment Effects on a Duration Outcome
研究了当治疗时间非随机分配且结果变量为持续时间时，如何利用工具变量识别和估计治疗的因果效应，并提出了估计方法及其渐近性质。
This article considers identification and estimation of the causal effect of the time Z until a subject is treated on a duration T. The time-to-treatment is not randomly assigned, T is randomly right censored by a random variable C, and the time-to-treatment Z is right censored by T∧C. The endogeneity issue is treated using an instrumental variable explaining Z and independent of the error term of the model. We study identification in a fully nonparametric framework. We show that our specification generates an integral equation, of which the regression function of interest is a solution. We provide identification conditions that rely on this identification equation. We assume that the regression function follows a parametric model for estimation purposes. We propose an estimation procedure and give conditions under which the estimator is asymptotically normal. The estimators exhibit good finite sample properties in simulations. Our methodology is applied to evaluate the effect of the timing of a therapy for burnout.