2023 Distinguished Lecture Series in Statistical Sciences: Eric J. Tchetgen Tchetgen
Join us for this year’s Distinguished Lecture Series in Statistical Sciences with:
Eric J. Tchetgen Tchetgen
Luddy Family President’s Distinguished Professor
Professor of Statistics and Data Science
University of Pennsylvania
Speaker Profile
Dr. Tchetgen Tchetgen is a graduate of Yale University and Harvard University where he received degrees in Applied Mathematics and Biostatistics respectively. After receiving his PhD in 2006, he was a Yerby Postdoctoral Fellow at Harvard University before serving on the faculty from 2008 to 2017. In 2018 Dr. Tchetgen Tchetgen was named the inaugural Luddy Family President’s Distinguished Professor in the Department of Statistics at The Wharton School of the University of Pennsylvania. He remains an Adjunct Professor of Biostatistics and Epidemiologic Methods at the Harvard Chan School.
Dr. Tchetgen Tchetgen has distinguished himself as one of the leading young biostatisticians and epidemiologic methodologists in the world, having made numerous influential contributions to the development and application of statistical methods for missing data, causal inference, and semiparametric regression in social, genetic and infectious disease epidemiology.
In addition to his myriad of research accomplishments, Dr. Tchetgen Tchetgen is a talented and inspiring teacher and mentor who has published well over 200 papers in top statistical, epidemiological and medical journals, produced an impressive record of grant funding, and has generously and tirelessly served the statistical profession, both nationally and internationally. He is a hardworking, creative, and well-respected leader, and through his statistical talent, has dedicated his career to advancing public health. He was awarded the inaugural Rousseeuw Prize for Statistics in 2022 for his contributions to causal inference and its applications in Medicine and Public Health.
Talk Title: An (un)Holy Union: Causal Inference, Semiparametric Statistics and Machine Learning in the Age of Data Science
Abstract: In this talk, I discuss recent advances at the nexus of Causal Inference, Semiparametric Statistics and Machine Learning in the gilded age of modern data science,with emphasis given to sound and responsible decision-making in Public Health and Medicine. I argue with several recent developments, that important scientific progress can be made more likely by fruitful interaction between these three disciplines than without; however, such success will require constant vigilance to ensure that key foundational principles underpinning each discipline are preserved and respected. For illustration, I consider the potential utility and limitations of several recent advances emblematizing this (un)holy union, including conformal predictive causal inference, Double-debiased machine learning, Credence and the so-called Deconfounder.