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.
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