Statistical Bioinformatics Seminar: Tchetgen Tchetgen -- Introduction to proximal causal learning

This is a hybrid event.  In-person in the Mackenzie Seminar Room Level 6, Charles
Perkins Centre and online via Zoom: https://uni-sydney.zoom.us/j/85114748391 

Abstract: 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.  One can therefore only ever hope that
covariate measurements are at best proxies of true underlying confounding mechanisms
operating in an observational study, thus invalidating causal claims made on basis of
standard exchangeability conditions.  Causal learning from proxies is a challenging
inverse problem which has to date remained unresolved.  In this paper, we introduce a
formal potential outcome framework for proximal causal learning, 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
basis of measured covariates fails.  Sufficient conditions for nonparametric
identification are given and the methods are applied to an evaluation of the causal
effect of covid-19 vaccination using observational data.  

About the speaker: Eric J Tchetgen Tchetgen Professor of Biostatistics in Biostatistics
and Epidemiology 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 of the University of Pennsylvania.  He also 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 Public Health and Medicine and of the inaugural 2025 David R Cox Medal
for Statistics awarded jointly by the Royal Statistical Society, the American
Statistical Association, the Bernoulli Society, the International Biometric Society, the
Institute of Mathematical Statistics and the International Statistical Institute.