Comments on “Unobservable Selection and Coefficient Stability: Theory and Evidence” and “Poorly Measured Confounders are More Useful on the Left Than on the Right”

We establish a link between the approaches proposed by Oster (2019) and Pei, Pischke, and Schwandt (2019) which contribute to the development of inferential procedures for causal effects in the challenging and empirically relevant situation where the unknown data-generation process is not included in the set of models considered by the investigator. We use the general misspecification framework recently proposed by De Luca, Magnus, and Peracchi (2018) to analyze and understand the implications of the restrictions imposed by the two approaches.

本项研究构建了Oster（2019年）及Pei、Pischke和Schwandt（2019年）所提出的方法论之间的联系，这些方法有助于在因果效应推断方面的发展，特别是在数据生成过程未知且未纳入研究者考虑的模型集合中的具有挑战性和实证相关性的情境下。本研究采用了De Luca、Magnus和Peracchi（2018年）近期提出的广义误设定框架，以分析和解读这两种方法所施加的限制的影响。