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DESCRIPTION:Eric J. Tchetgen Tchetgen\, PhD\n\nProfessor of Statistics and 
 Data Science | Wharton School |\n	University of Pennsylvania\n\nWhere: Virt
 ual | Zoom\n\nAbstract\n\nA standard assumption for causal inference from 
 observational data is that one has measured a sufficiently rich set of cov
 ariates to ensure that within covariates strata\, subjects are exchangeabl
 e across observed treatment values. Skepticism about the exchangeability a
 ssumption in observational studies is often warranted because it hinges on
  one's ability to accurately measure covariates capturing all potential so
 urces of confounding. Realistically\, confounding mechanisms can rarely if
  ever\, be learned with certainty from measured covariates. One can theref
 ore only ever hope that covariate measurements are at best proxies of true
  underlying confounding mechanisms operating in an observational study\, t
 hus invalidating causal claims made on basis of standard exchangeability c
 onditions. Causal learning from proxies is a challenging inverse problem w
 hich has to date remained unresolved. In this paper\, we introduce a forma
 l potential outcome framework for proximal causal learning\, which while e
 xplicitly acknowledging covariate measurements as imperfect proxies of con
 founding mechanisms\, offers an opportunity to learn about causal effects 
 in settings where exchangeability on basis of measured covariates fails. S
 ufficient conditions for nonparametric identification are given\, leading 
 to the proximal g-formula and corresponding proximal g-computation algorit
 hm for estimation\, both generalizations of Robins' foundational g-formula
  and g-computation algorithm\, which account explicitly for bias due to un
 measured confounding. Both point treatment and time-varying treatment sett
 ings are considered\, and an application of proximal g-computation of caus
 al effects is given for illustration.\n\nLearning Objectives\n\nReasoning 
 about unmeasured confounding using proxies\n	Nonparametric identification\n
 	g-computation\nSpeaker Bio\n\nEric J Tchetgen Tchetgen is The Luddy Family
  President's Distinguished Professor and Professor of Statistics and Data 
 Science at the Wharton School of the University of Pennsylvania. He also c
 o-directs the Penn Center for Causal Inference\, which supports the develo
 pment and dissemination of causal inference methods in Health and Social S
 ciences. He has published extensively on Causal Inference\, Missing Data a
 nd 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 i
 ndustry space. Professor Tchetgen Tchetgen is an 2022 inaugural co-recipie
 nt of the newly established Rousseeuw Prize for statistics in recognition 
 for his work in Causal Inference with applications in Public Health and Me
 dicine.\n\n \n\n \n\n \n\n \n\n \n
DTSTART:20230313T200000Z
DTEND:20230313T210000Z
SUMMARY:An Introduction to Proximal Causal Inference
URL:https://www.mcgill.ca/channels/channels/event/introduction-proximal-cau
 sal-inference-346771
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