Doubly robust identification for causal panel data models
研究面板数据中因果效应的识别与估计，提出基于处理分配与未观测混杂因素关系的识别假设，并开发双重稳健方法，适用于需要处理混杂偏倚的实证研究。
Summary We study identification and estimation of causal effects in settings with panel data. Traditionally, researchers follow model-based identification strategies relying on assumptions governing the relation between the potential outcomes and the observed and unobserved confounders. We focus on a different, complementary approach to identification, where assumptions are made about the connection between the treatment assignment and the unobserved confounders. Such strategies are common in cross-section settings, but rarely used with panel data. We introduce different sets of assumptions that follow the two paths to identification and develop a double robust approach. We propose estimation methods that build on these identification strategies.