What is he best known for?
The fields of study he is best known for:
- Statistics
- Regression analysis
- Machine learning
Guido W. Imbens mostly deals with Statistics, Econometrics, Estimator, Instrumental variable and Matching.
Average treatment effect, Propensity score matching, Causal inference, Regression analysis and Confidence interval are the subjects of his Statistics studies.
His Econometrics research is multidisciplinary, incorporating perspectives in Causality and Inference.
The study incorporates disciplines such as Range, Simultaneous equations model, Regression and Local regression in addition to Estimator.
His research integrates issues of Randomized experiment, Rubin causal model, Estimation and Identification in his study of Instrumental variable.
His Matching study combines topics from a wide range of disciplines, such as Endogeneity, Nonparametric statistics and Consistent estimator.
His most cited work include:
- Identification of Causal Effects Using Instrumental Variables (3383 citations)
- Recent developments in the econometrics of program evaluation (2594 citations)
- Identification and Estimation of Local Average Treatment Effects (2536 citations)
What are the main themes of his work throughout his whole career to date?
Guido W. Imbens spends much of his time researching Econometrics, Statistics, Estimator, Average treatment effect and Instrumental variable.
His research in Econometrics tackles topics such as Estimation which are related to areas like Outcome.
His Statistics study deals with Inference intersecting with Contrast and Lasso.
His work in Estimator covers topics such as Regression which are related to areas like Random forest.
In his study, Machine learning is inextricably linked to Causal inference, which falls within the broad field of Average treatment effect.
The Instrumental variable study combines topics in areas such as Simultaneous equations model, Supply and demand, Randomized experiment, Rubin causal model and Least squares.
He most often published in these fields:
- Econometrics (46.65%)
- Statistics (45.16%)
- Estimator (34.49%)
What were the highlights of his more recent work (between 2017-2021)?
- Estimator (34.49%)
- Econometrics (46.65%)
- Statistics (45.16%)
In recent papers he was focusing on the following fields of study:
Guido W. Imbens focuses on Estimator, Econometrics, Statistics, Average treatment effect and Panel data.
His research integrates issues of Random assignment, Covariate, Fixed effects model and Robustness in his study of Estimator.
His Difference in differences study in the realm of Econometrics interacts with subjects such as Earnings.
His research in the fields of Observational study, Sample and Propensity score matching overlaps with other disciplines such as Limit.
His Average treatment effect study combines topics in areas such as Machine learning and Artificial intelligence.
His Panel data research includes themes of Ensemble learning, Double robust, Relation and Identification.
Between 2017 and 2021, his most popular works were:
- Redefine statistical significance (993 citations)
- Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs (294 citations)
- Approximate residual balancing: debiased inference of average treatment effects in high dimensions (126 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Regression analysis
Guido W. Imbens mainly investigates Econometrics, Statistics, Estimator, Causal inference and Panel data.
His Econometrics research is multidisciplinary, incorporating perspectives in Replication crisis and Statistical significance.
His work carried out in the field of Statistics brings together such families of science as Inference and Forcing.
His Estimator research is multidisciplinary, relying on both Fixed effects model, Robustness and Difference in differences.
Average treatment effect is closely connected to Bias of an estimator in his research, which is encompassed under the umbrella topic of Difference in differences.
The study incorporates disciplines such as Observational study, Machine learning and Artificial intelligence in addition to Causal inference.
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