Treatment Effects With Unobserved Heterogeneity: A Set Identification Approach
提出利用面板数据对潜在结果分布进行尖锐可识别边界估计的方法，允许处理分配在控制未观测异质性后仍非随机，并应用于研究孕期吸烟对婴儿出生体重的影响。
We propose the sharp identifiable bounds of the potential outcome distributions using panel data. We allow for the possibility that statistical randomization of treatment assignments is not achieved until unobserved heterogeneity is properly controlled for. We use certain stationarity assumptions to obtain the sharp bounds. Our approach allows for dynamic treatment decisions, where the current treatment decisions may depend on the past treatments or the past observed outcomes. As an empirical illustration, we study the effect of smoking during pregnancy on infant birthweight. We find that for the group of switchers the infant birthweight of a smoking mother is first-order stochastically dominated by that of a nonsmoking mother.