The PCI Lab hosts a causal learning group consisting of students and faculty from diverse disciplines including statistics, epidemiology, political science, sociology, computer science, and more. Meetings are typically held on Fridays from 11:00 am – 12:15 pm PST. If you are interested in joining us, click here to be added to our email distribution list.
Additionally, you can find a listing of past and upcoming events
below.
2024
Regression-Based Proximal Causal Inference
Proximal Causal Inference
Join us over Zoom for a talk with Dr. Eric Tchetgen Tchetgen on regression-based proximal causal inference (see details below).
When: May 10th @ 11:00am - 12:15pm PST
How to Join: Click on this LINK at the start of the event.
About this Talk: In observational studies, identification of causal effects is threatened by the potential for unmeasured confounding. Negative controls have become widely used to evaluate the presence of potential unmeasured confounding thus enhancing credibility of reported causal effect estimates. Going beyond simply testing for residual confounding, proximal causal inference (PCI) was recently developed to debias causal effect estimates subject to confounding by hidden factors, by leveraging a pair of negative control variables, also known as treatment and outcome confounding proxies. While formal statistical inference has been developed for PCI, these methods can be challenging to implement in practice as they involve solving complex integral equations that are typically ill-posed. In this paper, we develop a regression-based PCI approach, employing a two-stage regression via familiar generalized linear models to implement the PCI framework, which completely obviates the need to solve difficult integral equations. In the first stage, one fits a generalized linear model (GLM) for the outcome confounding proxy in terms of the treatment confounding proxy and the primary treatment. In the second stage, one fits a GLM for the primary outcome in terms of the primary treatment, using the predicted value of the first-stage regression model as a regressor which as we establish accounts for any residual confounding for which the proxies are relevant. The proposed approach has merit in that (i) it is applicable to continuous, count, and binary outcomes cases, making it relevant to a wide range of real-world applications, and (ii) it is easy to implement using off-the-shelf software for GLMs. We establish the statistical properties of regression-based PCI and illustrate their performance in both synthetic and real-world empirical applications.
About Dr. Tchetgen Tchetgen: Eric J. Tchetgen Tchetgen is The University Professor, Professor of Biostatistics at the Perelman School of Medicine and Professor of Statistics and Data Science at The Wharton School at the University of Pennsylvania. He co-directs the Penn Center for Causal Inference, which supports the development and dissemination of causal inference methods in Health and Social Sciences. He has published extensively on Causal Inference, Missing Data and Semiparametric Theory with several impactful applications ranging from HIV research, Genetic Epidemiology, Environmental Health and Alzheimer's Disease and related aging disorders. He is an Amazon scholar working with Amazon scientists on a variety of causal inference problems in the Tech industry space. Professor Tchetgen Tchetgen is an 2022 inaugural co-recipient of the newly established Rousseeuw Prize for statistics in recognition for his work in Causal Inference with applications in Medicine and Public Health.