Title: An Introduction to Negative Control and Proximal Causal Learning
Summary:
A standard assumption for causal inference from observational data is that one has measured a sufficiently rich set of covariates to ensure that within covariates strata, subjects are exchangeable across observed treatment values. Skepticism about the exchangeability assumption in observational studies is often warranted because it hinges on one’s ability to accurately measure covariates capturing all potential sources of confounding. Realistically, confounding mechanisms can rarely if ever, be learned with certainty from measured covariates.
Negative controls are auxiliary variables not causally associated with the primary treatment or outcome, which have been used for adjustment for confounding bias in epidemiological research. In this talk, we first introduce a formal negative control study design, then we briefly review and summarize existing negative control methods for detection, reduction, and correction of confounding bias. We then introduce the proximal causal learning framework, a generalization of negative controls, which while explicitly acknowledging covariate measurements as imperfect proxies of confounding mechanisms, offers an opportunity to learn about causal effects in settings where exchangeability on the basis of measured covariates fails. We provide sufficient conditions for identification, leading to the proximal g-formula and corresponding proximal g-computation algorithm for estimation, both generalizations of Robins’ foundational g-formula and g-computation algorithm. We close with simulations and a data application of proximal g-computation of causal effects.
Speakers:
Eric Tchetgen Tchetgen’s primary area of interest is in semi-parametric efficiency theory with application to causal inference and missing data problems. In general, he works on the development and application of statistical and epidemiologic methods that make efficient use of the information in data collected by scientific investigators, while avoiding unnecessary assumptions about underlying data generating mechanisms. In 2018, Eric Tchetgen Tchetgen joined The Wharton School, University of Pennsylvania as the Luddy Family President’s Distinguished Professor and Professor of Statistics. Prior to that he was Professor of Biostatistics and Epidemiologic Methods at Harvard University. He completed his PhD in Biostatistics at Harvard University in 2006 received his B.S. in Electrical Engineering from Yale University in 1999 https://statistics.wharton.upenn.edu/profile/ett/
Xu Shi is an Assistant Professor in the Department of Biostatistics at University of Michigan. Her research focuses on developing novel statistical methods that provide insights from high volume and high variability administrative healthcare data such as the electronic health records (EHR) data. She is particularly interested in developing causal inference methods tailored to EHR data, automated knowledge extraction, data harmonization across healthcare systems, and post-marketing drug safety surveillance http://www.xuritashi.com
Wang Miao is Assistant Professor in the Department of Probability and Statistics at Peking University. His research centers around causal inference, missing data, data fusion, and their application in epidemiological and biomedical studies. In particular, he has been working on developing novel methods for confounding adjustment, analysis of nonignorable missing data, and two sample inference https://www.math.pku.edu.cn/teachers/mwfy/
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