Myrto Award

The Myrto Lefkopoulou Distinguished Lectureship

This Year’s Winner

We are extremely pleased to announce that Department of Biostatistics alumnus Dr. Eric Tchetgen Tchetgen will be the recipient of the 2020 Myrto Lefkopoulou Distinguished Lectureship.

Please join us for his virtual lecture on:
Thursday, September 17, 2020 | 1-2pm
**We will be posting a recording of this talk ASAP!**

An introduction to Proximal Causal Learning

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 relevant to various potential confounding mechanisms. 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 talk, I will 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 the basis of measured covariates fails. Sufficient conditions for nonparametric identification are given, leading to the proximal g-formula and a corresponding proximal g-computation algorithm for estimation, both generalizations of Robins’ foundational g-formula and g-computation algorithm, which explicitly account for residual confounding bias. Both point treatment and time-varying treatment settings are considered, and an application of proximal g-computation of causal effects is given as illustration.

Dr. Tchetgen Tchetgen is a graduate of the Department of Biostatistics at the Harvard T.H. Chan School of Public Health, having received his PhD in 2006 upon defending his dissertation on “Statistical Methods for Robust Inference in Causal and Missing Data Models” under his advisor Dr. Jamie Robins. After receiving his PhD, he was a Yerby Fellow at the School from 2006-2008 before serving on the faculty from 2008-2017. 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 over 140 papers in top statistical and epidemiological 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.


Highlights of Dr. Tchetgen Tchetgen’s honors and achievements:

  • Luddy Family President’s Distinguished Professor, Wharton Business School, University of Pennsylvania
  • 2020 Amazon Scholar
  • Published over 140 papers in top statistical journals and epidemiological journals, such as JASA, Biometrika, Annals of Statistics, Biometrics, and Biostatistics
  • Co-PI of the Botswana Combination Prevention Project, a major randomized trial of prevention of HIV infection in southern Africa
  • Founding Co-Editor: Epidemiologic Methods
  • Editorial Board Member for: Journal of the American Statistical Association, Journal of the Royal Society of Statistics, Series B , American Journal of Epidemiology, Biostatistics, International Journal of Biostatistics, Statistical Science, Journal of Causal Inference
  • Career Incubator Award, Harvard T.H. Chan School of Public Health
  • Standing member of the NIAID AIDS Clinical Studies and Epidemiology Study Section (ACE)
  • Member of three NIH expert panels on quantitative methods
  • Appointment as expert statistician for the Food and Drug Administration Arthritis Advisory Committee
  • Grant reviewer for the National Science Foundation, the American Mathematical Society, and the European Commission’s Directorate General for Research and Innovation
  • Winner of Society of Epidemiologic Research and American Journal of Epidemiology Article of the Year 2014 for coauthoring “Assessment and indirect adjustment for confounding by smoking in
    cohort studies using relative hazards model
  • Winner of the Kenneth Rothman Epidemiology Prize 2011 for co-authoring  “The use of negative controls to detect confounding and other sources of error in experimental and observational science

 

About the Award

myrtoThe Myrto Lefkopoulou Distinguished Lectureship was established in perpetuity in memory of Dr. Myrto Lefkopoulou, a faculty member and graduate of Harvard School of Public Health. Dr. Lefkopoulou tragically died of cancer in 1992 at the age of 34 after a courageous two-year battle. She was deeply beloved by friends, students, and faculty.

Each year the lectureship is awarded to a promising statistician who has made contributions to either collaborative or methodologic research in the applications of statistical methods to biology or medicine, and/or who has shown excellence in the teaching of biostatistics. Ordinarily, the lectureship is given to a statistician who has earned a doctorate in the last fifteen years. The lecture is presented to a general scientific audience as the first Department colloquium of each academic year. The lectureship includes travel to Boston, a reception following the lecture, and an honorarium of $1000.

Nominations

Please send nominations via email

or by mail to:

Myrto Lefkopoulou Committee
Harvard T. H. Chan School of Public Health
Department of Biostatistics
Building 2, 4th Floor
655 Huntington Avenue
Boston, MA 02115

Nominations should include a letter of nomination and a C.V.

All nominations must be received by June 5, 2020.

Past Recipients

2019  Veera Baladandayuthapani
2018  Elizabeth Stuart
2017  Ciprian Crainiceanu
2016  Mahlet Tadesse
2015  Debashis Ghosh
2014  Tianxi Cai
2013  Nilanjan Chatterjee
2012  Rafael Irizarry
2011  Jeffrey Morris
2010  David Dunson
2009  Xihong Lin
2008  Heping Zhang
2007  Francesca Dominici
2006  Jianqing Fan
2005  Mark van der Laan
2004  Geert Molenberghs
2003  Marie Davidian
2002  Danyu Lin
2001  Bradley P. Carlin
2000  Steven N. Goodman
1999  Giovanni Parmigianni
1998  Kathryn Roeder
1997  Ronald S. Brookmeyer
1996  Trevor J. Hastie
1995  Hans-George Mueller
1994  Michael L. Boehnke
1993  Louise Ryan