Eric J. Tchetgen Tchetgen
- University Professor
- Professor of Biostatistics in Biostatistics and Epidemiology
- Professor of Statistics and Data Science
Contact Information
- Primary Email:
ett@wharton.upenn.edu - Office Phone:
215-746-4328
- office Address:
407 Academic Research Building
265 South 37th Street
Philadelphia, PA 19104
Research Interests: Semiparametric theory, nonparametric statistics, causal inference, missing data, and epidemiologic methods.
Overview
Education
Ph.D., 2006, Harvard University
B.S., 1999, Yale University
Research
My primary area of interest is in semi-parametric efficiency theory with application to causal inference, missing data problems, statistical genetics and mixed model theory. In general, I work on the development of statistical and epidemiologic methods that make efficient use of the information in data collected by scientific investigators, while avoiding unnecessary assumptions about the underlying data generating mechanism.
Research
Hongxiang Qiu, Eric Tchetgen Tchetgen, Edgar Dobriban Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shift.
Hongxiang Qiu, Xu Shi, Wang Miao, Edgar Dobriban, Eric Tchetgen Tchetgen, Doubly Robust Proximal Synthetic Controls.
Description: https://arxiv.org/abs/2210.02014
Hongxiang Qiu, Edgar Dobriban, Eric Tchetgen Tchetgen (Draft), Distribution-free Prediction Sets Adaptive to Unknown Covariate Shift.
Abstract: Predicting sets of outcomes -- instead of unique outcomes -- is a promising solution to uncertainty quantification in statistical learning. Despite a rich literature on constructing prediction sets with statistical guarantees, adapting to unknown covariate shift -- a prevalent issue in practice -- poses a serious challenge and has yet to be solved. In the framework of semiparametric statistics, we can view the covariate shift as a nuisance parameter. In this paper, we propose a novel flexible distribution-free method, PredSet-1Step, to construct prediction sets that can efficiently adapt to unknown covariate shift. PredSet-1Step relies on a one-step correction of the plug-in estimator of coverage error. We theoretically show that our methods are asymptotically probably approximately correct (PAC), having low coverage error with high confidence for large samples. PredSet-1Step may also be used to construct asymptotically risk-controlling prediction sets. We illustrate that our method has good coverage in a number of experiments and by analyzing a data set concerning HIV risk prediction in a South African cohort study. In experiments without covariate shift, PredSet-1Step performs similarly to inductive conformal prediction, which has finite-sample PAC properties. Thus, PredSet-1Step may be used in the common scenario if the user suspects -- but may not be certain -- that covariate shift is present, and does not know the form of the shift. Our theory hinges on a new bound for the convergence rate of Wald confidence interval coverage for general asymptotically linear estimators. This is a technical tool of independent interest.
Yifan Cui and Eric Tchetgen Tchetgen (Working), Selective machine learning of doubly robust functionals.
Yifan Cui and Eric Tchetgen Tchetgen (2021), A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity, Journal of the American Statistical Association, 116 (133), pp. 162-173.
Wey Wen Lim, Nancy H L Leung, Sheena G. Sullivan, Eric Tchetgen Tchetgen, Benjamin J. Cowling (2020), Distinguishing Causation from Correlation in the Use of Correlates of Protection to Evaluate and Develop Influenza Vaccines, American Journal of Epidemiology, (to appear).
Tom Chen, Eric Tchetgen Tchetgen, Rui Wang (2020), A Stochastic Second-Order Generalized Estimating Equations Approach for Estimating Association Parameters, Journal of Computational and Graphical Statistics , (to appear).
Haben Michael, Yifan Cui, Scott A. Lorch, Eric Tchetgen Tchetgen (Working), Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.
Caleb H. Miles, Ilya Shpitser, Phyllis Kanki, Seema Meloni, Eric Tchetgen Tchetgen (2020), On semiparametric estimation of a path-specific effect in the presence of mediator-outcome confounding, Biometrika, 107 (1), pp. 159-172.
Lan Liu and Eric Tchetgen Tchetgen (Working), Regression-based Negative Control of Homophily in Dyadic Peer Effect Analysis.
Teaching
All Courses
BSTA6990 - Lab Rotation
Student lab rotation.
BSTA8990 - Pre-Dissertation Lab Rot
PSYC6120 - Int To Nonp & Loglin Mod
An applied graduate level course for students who have completed an undergraduate course in basic statistical methods. Covers two unrelated topics: loglinear and logit models for discrete data and nonparametric methods for nonnormal data. Emphasis is on practical methods of data analysis and their interpretation. Primarily for doctoral students in the managerial, behavioral, social and health sciences. Permission of instructor required to enroll.
STAT3990 - Independent Study
Written permission of instructor and the department course coordinator required to enroll in this course.
STAT5010 - Int To Nonp & Loglin Mod
An applied graduate level course for students who have completed an undergraduate course in basic statistical methods. Covers two unrelated topics: loglinear and logit models for discrete data and nonparametric methods for nonnormal data. Emphasis is on practical methods of data analysis and their interpretation. Primarily for doctoral students in the managerial, behavioral, social and health sciences. Permission of instructor required to enroll.
STAT9210 - Observational Studies
This course will cover statistical methods for the design and analysis of observational studies. Topics will include the potential outcomes framework for causal inference; randomized experiments; matching and propensity score methods for controlling confounding in observational studies; tests of hidden bias; sensitivity analysis; and instrumental variables.
STAT9620 - Adv Methods Applied Stat
This course is designed for Ph.D. students in statistics and will cover various advanced methods and models that are useful in applied statistics. Topics for the course will include missing data, measurement error, nonlinear and generalized linear regression models, survival analysis, experimental design, longitudinal studies, building R packages and reproducible research.
STAT9910 - Sem in Adv Appl of Stat
This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.
STAT9950 - Dissertation
STAT9990 - Independent Study
Written permission of instructor and the department course coordinator required to enroll.
Awards and Honors
- Co-winner of the Rousseeuw Prize for Statistics, 2022
- Myrto Lefkopoulou Distinguished Lectureship, 2020
- Co-winner of the Society of Epidemiologic Research and American Journal of Epidemiology Article of the Year, 2014 Description
For the paper, “Assessment and indirect adjustment for confounding by smoking in cohort studies using relative hazards model” with David Richardson, Steve Cole
and Dominique Laurier. - Career Incubator Award, Harvard School of Public Health, 2013-2014
- Co-winner of the Kenneth Rothman Epidemiology Prize, 2011 Description
For the paper, “The use of negative controls to detect confounding and other sources of error in experimental and observational science.” with Marc Lipsitch and Ted Cohen.
- Best Poster Award: Gene Environment Initiative Symposium, Boston, MA, 2008
- Yerby Fellowship, Harvard School of Public Health, 2006-2008
- Mars Scholar, Yale University, 1995-1996
In the News
- Intensive Anti-H.I.V. Efforts Meet With Mixed Success in Africa, New York Times - 07/17/2019 Description
Scientists tested a costly approach to curbing the AIDS epidemic: Test everyone in the community, and treat anyone who is infected.
Activity
Latest Research
In the News
Scientists tested a costly approach to curbing the AIDS epidemic: Test everyone in the community, and treat anyone who is infected.
New York Times - 07/17/2019