Causal Inference Postdoctoral Researcher - Center Causal Inference, University of Pennsylvania

University of Pennsylvania:
Postdoctoral Positions:
Perelman School of Medicine Postdoctoral

University of Pennsylvania - School of Medicine

Dec 02, 2025

Faculty Mentors:
Dr. Eric Tchetgen Tchetgen and Dr. Nandita Mitra.

Department:
The Center for Causal Inference (CCI) in the Department of Biostatistics, Epidemiology & Informatics (DBEI)

This position is open to applications from US citizens and foreign nationals.

The Center for Causal Inference (CCI) is a research center in the Department of Biostatistics, Epidemiology and Informatics in the Perelman School of Medicine of the University of Pennsylvania (https://(Use the "Apply for this Job" box below).) with close collaboration with the Wharton Statistics Department. The CCI is a multidisciplinary center that includes faculty, postdocs, and graduate students from Biostatistics, Statistics, Epidemiology, Criminology, Computer Science, Philosophy, and other disciplines.

The mission of the CCI is to be the leading center for research and training in the development and application of causal inference theory and methods, with far-ranging applications in Health and Social Sciences.

The position is for a postdoctoral researcher in causal inference with considerable flexibility in terms of research focus, reflecting the interdisciplinary nature of the field. In particular, the postdoctoral researcher will have an opportunity to collaborate on a broad range of methodological projects under the mentorship of one or more faculty affiliated with CCI.

In addition, the researcher will receive mentorship for professional development from the CCI co-directors Professors Nandita Mitra and Eric Tchetgen Tchetgen, as well as dedicated mentorship based on project type, with the expectation that their time as a researcher will advance their career development.

• Developing methods to study the effect of antidiabetic treatments on the recurrence of hospitalizations for T2DM-related complications.
• Developing methods for constructing just-in-time adaptive interventions using mobile health (mHealth) data.
• Developing novel causal inference methods to reconcile possibly conflicting results between randomized trials or randomized trials and observational studies, particularly in the area of pregnancy research.

develop new theory and methods in causal inference; develop software; and potentially collaborate on applied projects in Health and Social Sciences. In addition, the fellow is expected to be an active member of the CCI, by, for example, participating in the weekly working group meeting and the annual Causal Inference Summer Institute.

Candidates should have a doctoral degree in biostatistics, statistics, or a related field. Strong computational skills and expertise in causal inference are desired but not required. Start date and term are negotiable.