What is she best known for?
The fields of study she is best known for:
- Statistics
- Law
- Microeconomics
Susan Athey mostly deals with Econometrics, Common value auction, Microeconomics, Statistics and Bidding.
Susan Athey interconnects Inference, External validity, Economic model and Identification in the investigation of issues within Econometrics.
Her biological study spans a wide range of topics, including Random forest and Asymptotic distribution.
Her Common value auction research includes themes of Mathematical economics, Game theory, Nash equilibrium and Complete information.
Her study explores the link between Microeconomics and topics such as Private information retrieval that cross with problems in Discretion, Social welfare function and Mechanism design.
Her work deals with themes such as Best response, Strategy, Service and Revenue, which intersect with Bidding.
Her most cited work include:
- Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (577 citations)
- Identification and Inference in Nonlinear Difference-in-Differences Models (524 citations)
- Single Crossing Properties and the Existence of Pure Strategy Equilibria in Games of Incomplete Information (415 citations)
What are the main themes of her work throughout her whole career to date?
Susan Athey focuses on Econometrics, Microeconomics, Inference, Estimator and Common value auction.
Her Econometrics study integrates concerns from other disciplines, such as Random assignment and Identification.
Her study in the field of Collusion, Incentive and Shock is also linked to topics like Value.
Her Inference study combines topics in areas such as Counterfactual thinking and Contrast, Regression, Statistics, Sample.
Susan Athey has included themes like Mathematical optimization and Applied mathematics in her Estimator study.
Her Common value auction study combines topics in areas such as Bidding, Mathematical economics and Revenue.
She most often published in these fields:
- Econometrics (20.13%)
- Microeconomics (14.09%)
- Inference (15.10%)
What were the highlights of her more recent work (between 2018-2021)?
- Estimator (13.76%)
- Artificial intelligence (13.76%)
- Machine learning (12.42%)
In recent papers she was focusing on the following fields of study:
Her primary areas of study are Estimator, Artificial intelligence, Machine learning, Econometrics and Regression.
Her Estimator study combines topics from a wide range of disciplines, such as Mathematical optimization and Robustness.
Her Machine learning research focuses on subjects like Causal inference, which are linked to Relevance.
Her specific area of interest is Econometrics, where she studies Panel data.
The concepts of her Regression study are interwoven with issues in Random forest, Inference and Factor analysis.
The Inference study combines topics in areas such as Counterfactual thinking and Consumer choice.
Between 2018 and 2021, her most popular works were:
- Generalized random forests (287 citations)
- Machine Learning Methods That Economists Should Know About (88 citations)
- Machine Learning Methods That Economists Should Know About (88 citations)
In her most recent research, the most cited papers focused on:
- Statistics
- Law
- Microeconomics
Her primary areas of investigation include Machine learning, Artificial intelligence, Estimator, Inference and Causal inference.
She combines subjects such as Effective method, Counterfactual thinking and Consumer choice with her study of Machine learning.
Her Estimator research incorporates elements of Decision tree, Observational study, Instrumental variable and Leverage.
Her Inference research includes elements of Outcome and Regression.
Unsupervised learning is closely connected to Relevance in her research, which is encompassed under the umbrella topic of Causal inference.
Her Econometrics research extends to the thematically linked field of Average treatment effect.
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