Comments on “Unobservable Selection and Coefficient Stability: Theory and Evidence” and “Poorly Measured Confounders are More Useful on the Left Than on the Right”
建立了Oster（2019）与Pei、Pischke和Schwandt（2019）两种方法之间的联系，利用De Luca等（2018）的通用误设定框架分析其限制条件，帮助研究者理解在模型未包含真实数据生成过程时如何推断因果效应。
–We establish a link between the approaches proposed by Oster (2019 Oster, E. (2019), “Unobservable Selection and Coefficient Stability: Theory and Evidence,” Journal of Business and Economic Statistics, 37(2). DOI: 10.1080/07350015.2016.1227711.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) and Pei, Pischke, and Schwandt (2019 Pei, Z., Pischke, J.-S., and Schwandt, H. (2019), “Poorly Measured Confounders Are More Useful on the Left Than on the Right,” Journal of Business and Economic Statistics, 37(2). DOI: 10.1080/07350015.2018.1462710.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) 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 De Luca, G., Magnus, J. R., and Peracchi, F. (2018), “Balanced Variable Addition in Linear Models,” Journal of Economic Surveys, 32, 1183–1200. DOI: 10.1111/joes.12245.[Crossref], [Web of Science ®] , [Google Scholar]) to analyze and understand the implications of the restrictions imposed by the two approaches.