D-Index & Metrics Best Publications
Mathematics
USA
2023

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Mathematics D-index 109 Citations 59,578 293 World Ranking 14 National Ranking 8

Research.com Recognitions

Awards & Achievements

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)

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Internal medicine
  • Surgery

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.

His most cited work include:

  • Marginal Structural Models and Causal Inference in Epidemiology (3398 citations)
  • Causal diagrams for epidemiologic research. (2342 citations)
  • Estimation of Regression Coefficients When Some Regressors are not Always Observed (1728 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Statistics (34.46%)
  • Econometrics (31.08%)
  • Estimator (24.82%)

What were the highlights of his more recent work (between 2017-2021)?

  • Causal inference (18.55%)
  • Estimator (24.82%)
  • Statistics (34.46%)

In recent papers he was focusing on the following fields of study:

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.

Between 2017 and 2021, his most popular works were:

  • Partial Identification of the Average Treatment Effect Using Instrumental Variables: Review of Methods for Binary Instruments, Treatments, and Outcomes (34 citations)
  • A unifying approach for doubly-robust $ll_1$ regularized estimation of causal contrasts (19 citations)
  • Double/De-Biased Machine Learning Using Regularized Riesz Representers (18 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Internal medicine
  • Surgery

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Marginal Structural Models and Causal Inference in Epidemiology

James M. Robins;Miguel Ángel Hernán;Babette Brumback.
Epidemiology (2000)

4799 Citations

Causal diagrams for epidemiologic research.

Sander Greenland;Judea Pearl;James M. Robins.
Epidemiology (1999)

3357 Citations

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)

2765 Citations

A structural approach to selection bias.

Miguel A Hernán;Sonia Hernández-Díaz;James M Robins.
Epidemiology (2004)

2361 Citations

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.
Mathematical Modelling (1986)

2205 Citations

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)

1762 Citations

Transmission Dynamics and Control of Severe Acute Respiratory Syndrome

Marc Lipsitch;Ted Cohen;Ben Cooper;James M. Robins.
Science (2003)

1741 Citations

Identifiability and exchangeability for direct and indirect effects.

James M. Robins;Sander Greenland.
Epidemiology (1992)

1721 Citations

Doubly robust estimation in missing data and causal inference models

Heejung Bang;James M. Robins.
Biometrics (2005)

1692 Citations

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.
Epidemiology (2000)

1676 Citations

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