Citation
Zivich, Paul N.; Cole, Stephen R.; Edwards, Jessie K.; Mulholland, Grace E.; Shook-Sa, Bonnie E.; & Tchetgen Tchetgen, Eric J. (2023). Introducing Proximal Causal Inference for Epidemiologists. American Journal of Epidemiology, 192(7), 1224–7.Abstract
Causal inference with observational data has generally proceeded under the assumption of conditional exchangeability. That is, the action (e.g., treatment, exposure, intervention) is independent of the potential outcomes conditional on a set of covariates. However, exchangeability is often questionable. Miao, Geng, and Tchetgen Tchetgen have proposed an alternative approach to identification, a generalization of previous work by Kuroki and Pearl, which allows for unmeasured confounding with particular causal structures. Specifically, if there exists a measured variable that is a potential cause of the action and unrelated to the outcome except through measured confounders and a known but unmeasured confounder (i.e., treatment proxy), and another measured variable that is a potential cause of the same outcome and unrelated to the action except through measured confounders and the same unmeasured confounder (i.e., outcome proxy); then the average causal effect (ACE) can be identified nonparametrically under a set of sufficient conditions. Here, we briefly introduce proximal causal inference, where ‘proximal’ denotes that the pair of measured variables are consequences of the unmeasured confounder, to the epidemiology community and demonstrate its application using a simulation study.URL
http://dx.doi.org/10.1093/aje/kwad077Reference Type
Journal ArticleYear Published
2023Journal Title
American Journal of EpidemiologyAuthor(s)
Zivich, Paul N.Cole, Stephen R.
Edwards, Jessie K.
Mulholland, Grace E.
Shook-Sa, Bonnie E.
Tchetgen Tchetgen, Eric J.
Article Type
RegularORCiD
Zivich - 0000-0002-9932-1095Edwards, J -0000-0002-1741-335X