| 讲座简介： | Miao, Geng, and Tchetgen (2018) proposed a proximal inference approach for adjustment of unmeasured confounding, which broadens the set of structures under which the causal effect can be identified and estimated. In this talk, I will discuss the recent development and its application in synthetic control.Synthetic control methods attract increasing attention for estimating the treatment effect on a single treated unit in comparative case studies. A synthetic control (SC) is a weighted average of control units built to match the treated unit’s pre-treatment outcome trajectory, with weights typically estimated by regressing pre-treatment outcomes of the treated unit to those of the control units. However, it has been established that such regression estimators can fail to be consistent. In this paper, we introduce a proximal causal inference framework to formalize identification and inference for both the SC weights and the treatment effect on the treated. We also propose to view the difference in the post-treatment outcomes between the treated unit and the SC as a time series, which opens the door to a rich literature on time-series analysis for treatment effect estimation. We illustrate with an application to evaluation of the 1990 German Reunification.
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