2023 - 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)
Statistics, Econometrics, Causal inference, Estimator and Confounding are his primary areas of study. His biological study spans a wide range of topics, including Regression analysis, Inference and Selection. His studies deal with areas such as Dynamic treatment regime, Observational study, Inverse probability weighting, Counterfactual thinking and Causal model as well as Causal inference.
His Estimator research incorporates elements of Accelerated failure time model and Conditional expectation. James M. Robins has included themes like Surgery, Identifiability, Causality and Confidence interval in his Confounding study. In his research, Proportional hazards model is intimately related to Survival analysis, which falls under the overarching field of Covariate.
James M. Robins mostly deals with Statistics, Econometrics, Estimator, Causal inference and Confounding. His studies link Inference with Statistics. In the field of Econometrics, his study on Marginal structural model overlaps with subjects such as Inverse probability.
His Estimator course of study focuses on Missing data and Semiparametric model. His Causal inference study integrates concerns from other disciplines, such as Counterfactual thinking, Dynamic treatment regime, Causality, Directed acyclic graph and Causal model. His work focuses on many connections between Confounding and other disciplines, such as Observational study, that overlap with his field of interest in Randomized controlled trial and Average treatment effect.
His scientific interests lie mostly in Causal inference, Estimator, Statistics, Randomized controlled trial and Econometrics. His biological study focuses on Marginal structural model. James M. Robins combines subjects such as Contrast, Confidence interval, Interval, Applied mathematics and Conditional expectation with his study of Estimator.
His Randomized controlled trial research integrates issues from Observational methods in psychology, Counterfactual thinking and Red meat. His work in Econometrics addresses subjects such as Separable space, which are connected to disciplines such as Competing risks. His research in Efficient estimator focuses on subjects like Confounding, which are connected to Epidemiology.
His primary areas of investigation include Causal inference, Statistics, Randomized controlled trial, Estimator and Average treatment effect. James M. Robins studies Marginal structural model which is a part of Causal inference. His is doing research in Instrumental variable and Outcome, both of which are found in Statistics.
His research investigates the connection between Instrumental variable and topics such as Nonparametric statistics that intersect with issues in Inverse probability weighting and Missing data. His Estimator study often links to related topics such as Applied mathematics. His Separable space research is multidisciplinary, incorporating elements of Competing risks and Econometrics.
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Marginal Structural Models and Causal Inference in Epidemiology
James M. Robins;Miguel Ángel Hernán;Babette Brumback.
Causal diagrams for epidemiologic research.
Sander Greenland;Judea Pearl;James M. Robins.
Estimation of Regression Coefficients When Some Regressors are not Always Observed
James M. Robins;Andrea Rotnitzky;Lue Ping Zhao.
Journal of the American Statistical Association (1994)
A structural approach to selection bias.
Miguel A Hernán;Sonia Hernández-Díaz;James M Robins.
A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
Mathematical Modelling (1986)
Analysis of semiparametric regression models for repeated outcomes in the presence of missing data
James M. Robins;Andrea Rotnitzky;Lue Ping Zhao.
Journal of the American Statistical Association (1995)
Transmission Dynamics and Control of Severe Acute Respiratory Syndrome
Marc Lipsitch;Ted Cohen;Ben Cooper;James M. Robins.
Identifiability and exchangeability for direct and indirect effects.
James M. Robins;Sander Greenland.
Doubly robust estimation in missing data and causal inference models
Heejung Bang;James M. Robins.
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.
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