World's Best Scientists 2026 revealed!
Award Badge
Mathematics
USA
2026

D-Index & Metrics

Mathematics

D-Index
118
Citations
80437
World Ranking
16
National Ranking
11

Research.com Recognitions

  • 2026 - Research.com Mathematics in United States Leader Award
  • 2025 - Research.com Mathematics in United States Leader Award
  • 2015 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 1998 - Fellow of the American Statistical Association (ASA)

Overview

James M. Robins is affiliated with Harvard University in the United States. Their research primarily spans the field of Mathematics, with a strong focus on Statistics and Probability, along with contributions in Economics and Econometrics, Artificial Intelligence, General Health Professions, and Modeling and Simulation.

The main topics covered in Robins' work include:

  • Advanced Causal Inference Techniques
  • Statistical Methods and Bayesian Inference
  • Statistical Methods and Inference
  • Statistical Methods in Clinical Trials
  • Health Systems, Economic Evaluations, Quality of Life
  • Bayesian Modeling and Causal Inference
  • COVID-19 epidemiological studies

Robins has published extensively in various journals with frequent appearances in:

  • arXiv (Cornell University)
  • Biometrika
  • Biometrics
  • The International Journal of Biostatistics
  • Journal of the American Statistical Association

Key recent papers by Robins include:

  • "Locally Robust Semiparametric Estimation" (2022, Econometrica)
  • "Separable Effects for Causal Inference in the Presence of Competing Events" (2020, Journal of the American Statistical Association)
  • "Benchmarking Observational Methods by Comparing Randomized Trials and Their Emulations" (2020, Epidemiology)
  • "A generalized theory of separable effects in competing event settings" (2021, Lifetime Data Analysis)
  • "Potential Biases Arising From Epidemic Dynamics in Observational Seroprotection Studies" (2020, American Journal of Epidemiology)

Robins collaborates regularly with several researchers, including:

  • Miguel A. Hernán
  • Thomas S. Richardson
  • Jessica G. Young
  • Andrea Rotnitzky
  • Zach Shahn

In addition to journal articles, Robins has published books, such as "When We Dead Awaken" in 2020 with Bloomsbury Publishing plc.

The scientist has received recognition through fellowships, including being named a Fellow of the American Association for the Advancement of Science (AAAS) in 2015 and a Fellow of the American Statistical Association (ASA) in 1998.

Best Publications

  • Marginal Structural Models and Causal Inference in Epidemiology

    James M. Robins;Miguel Ángel Hernán;Babette Brumback

  • Estimation of Regression Coefficients When Some Regressors are not Always Observed

    James M. Robins;Andrea Rotnitzky;Lue Ping Zhao

  • A structural approach to selection bias.

    Miguel A Hernán;Sonia Hernández-Díaz;James M Robins

  • Double/Debiased Machine Learning for Treatment and Structural Parameters

    Victor Chernozhukov;Denis Chetverikov;Mert Demirer;Esther Duflo

  • A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect

    James Robins

  • Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available

    Miguel A. Hernán;James M. Robins

  • Doubly robust estimation in missing data and causal inference models

    Heejung Bang;James M. Robins

  • Identifiability and exchangeability for direct and indirect effects.

    James M. Robins;Sander Greenland

  • Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men.

    Miguel Angel Hernan;Babette Brumback;James M. Robins

  • Analysis of semiparametric regression models for repeated outcomes in the presence of missing data

    James M. Robins;Andrea Rotnitzky;Lue Ping Zhao

  • Transmission Dynamics and Control of Severe Acute Respiratory Syndrome

    Marc Lipsitch;Ted Cohen;Ben Cooper;James M. Robins

  • Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models

    Daniel O. Scharfstein;Andrea Rotnitzky;James M. Robins

  • Estimating causal effects from epidemiological data

    Miguel A Hernán;James M Robins

  • Unified Methods for Censored Longitudinal Data and Causality

    M. J. van der Laan;James M. Robins

  • Instruments for causal inference: an epidemiologist's dream?

    Miguel A. Hernan;James M. Robins

  • Semiparametric Efficiency in Multivariate Regression Models with Missing Data

    James M. Robins;Andrea Rotnitzky

  • Confounding and Collapsibility in Causal Inference

    Sander Greenland;James M. Robins;Judea Pearl

  • Transmissibility of 1918 pandemic influenza

    Christina E. Mills;James M. Robins;Marc Lipsitch

  • Correcting for noncompliance and dependent censoring in an AIDS Clinical Trial with inverse probability of censoring weighted (IPCW) log-rank tests.

    James M. Robins;Dianne M. Finkelstein

  • Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease

    Miguel A. Hernán;Alvaro Alonso;Roger Logan;Francine Grodstein

  • Estimation of a common effect parameter from sparse follow-up data.

    Sander Greenland;James M. Robins

  • Recovery of Information and Adjustment for Dependent Censoring Using Surrogate Markers

    James M. Robins;Andrea Rotnitzky

Frequent Co-Authors

Miguel A. Hernán
Miguel A. Hernán Harvard University
Andrea Rotnitzky
Andrea Rotnitzky University of Washington
Eric J. Tchetgen Tchetgen
Eric J. Tchetgen Tchetgen University of Pennsylvania
Thomas S. Richardson
Thomas S. Richardson University of Washington
Tyler J. VanderWeele
Tyler J. VanderWeele Harvard University
Sander Greenland
Sander Greenland University of California, Los Angeles
Larry Wasserman
Larry Wasserman Carnegie Mellon University
JoAnn E. Manson
JoAnn E. Manson Harvard Medical School
Caroline Sabin
Caroline Sabin University College London

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students interested in branching out from Mathematics, online degrees offer flexible and affordable opportunities to enhance career prospects. Pursuing an MBA can be a strategic move, and selecting the easiest online mba program may help balance workload and study demands efficiently.

For those aiming to deepen their expertise in business leadership, exploring dba programs online can provide advanced skills with budget-friendly options. These programs often complement analytical skills gained in Mathematics.

Finance is another related field where Mathematics graduates thrive. Finding the cheapest online masters in finance programs makes gaining advanced financial knowledge accessible without sacrificing affordability.

For professionals seeking accelerated study paths, the fastest online mba programs enable earning degrees quickly while maintaining work commitments. These options illustrate how flexible online education can be for Mathematics graduates exploring diverse career pathways.

Best Scientists Citing James M. Robins

Trending Scientists