What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Law
Donald B. Rubin mainly investigates Statistics, Econometrics, Rubin causal model, Observational study and Missing data.
His Econometrics study combines topics from a wide range of disciplines, such as Univariate, Linear regression, Frequentist inference, Bayesian inference and Categorical variable.
His Rubin causal model research incorporates themes from Average treatment effect, Covariate, Resampling and Principal stratification.
His Observational study study incorporates themes from Cognitive psychology, Sample size determination, Matching, Multivariate statistics and Propensity score matching.
His work on Imputation, Listwise deletion and Missing data imputation as part of his general Missing data study is frequently connected to Statistical analysis, thereby bridging the divide between different branches of science.
His Causal inference research incorporates elements of Randomized experiment and Causal model.
His most cited work include:
- Maximum likelihood from incomplete data via the EM algorithm (39248 citations)
- The central role of the propensity score in observational studies for causal effects (19229 citations)
- Statistical Analysis with Missing Data (17532 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Statistics, Econometrics, Missing data, Causal inference and Randomized experiment.
His study in Covariate, Imputation, Propensity score matching, Matching and Sample is carried out as part of his Statistics studies.
The concepts of his Econometrics study are interwoven with issues in Observational study, Inference and Bayesian probability.
His work on Missing data is being expanded to include thematically relevant topics such as Data mining.
While the research belongs to areas of Causal inference, Donald B. Rubin spends his time largely on the problem of Frequentist inference, intersecting his research to questions surrounding Statistical inference.
His work deals with themes such as Average treatment effect and Instrumental variable, which intersect with Randomized experiment.
He most often published in these fields:
- Statistics (43.66%)
- Econometrics (35.87%)
- Missing data (18.71%)
What were the highlights of his more recent work (between 2011-2021)?
- Statistics (43.66%)
- Econometrics (35.87%)
- Randomized experiment (15.20%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Statistics, Econometrics, Randomized experiment, Causal inference and Covariate.
In his research on the topic of Statistics, Likelihood function is strongly related with Inference.
In his work, Class is strongly intertwined with Bayesian probability, which is a subfield of Econometrics.
He interconnects Average treatment effect, Observational study, Instrumental variable, Mathematical optimization and Crossover in the investigation of issues within Randomized experiment.
His studies in Causal inference integrate themes in fields like Social media, Disinformation, Outcome and Frequentist inference.
His biological study spans a wide range of topics, including Treatment and control groups, Factorial, Propensity score matching, Mahalanobis distance and Factorial experiment.
Between 2011 and 2021, his most popular works were:
- Statistical Analysis with Missing Data, Second Edition (635 citations)
- Causal Inference for Statistics, Social, and Biomedical Sciences (608 citations)
- Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction (508 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Law
His primary areas of study are Statistics, Econometrics, Causal inference, Missing data and Covariate.
Donald B. Rubin combines subjects such as Bayesian probability, A priori and a posteriori and Joint probability distribution with his study of Econometrics.
His Causal inference research includes elements of Matching, Randomized experiment, Outcome and Frequentist inference.
His work on Imputation as part of general Missing data study is frequently linked to Complete data, therefore connecting diverse disciplines of science.
Donald B. Rubin has researched Imputation in several fields, including Univariate and Sample size determination.
His research integrates issues of Inference and Expectation–maximization algorithm in his study of Principal stratification.
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