Variable selection in double/debiased machine learning for causal inference: an outcome-adaptive approach
DOI10.1080/03610918.2021.2001655OpenAlexW3213752564WikidataQ130421075 ScholiaQ130421075MaRDI QIDQ6204952
Unnamed Author, Mototsugu Shintani
Publication date: 11 April 2024
Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610918.2021.2001655
high-dimensional datamachine learningcausal inferencedouble/debiased machine learningoutcome-adaptive Lasso
Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Causal inference from observational studies (62D20)
Cites Work
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- On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects
- Outcome‐adaptive lasso: Variable selection for causal inference
- Variable selection – A review and recommendations for the practicing statistician
- Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
- Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics
- Double/debiased machine learning for treatment and structural parameters
- Variable Selection Methods in High-dimensional Regression—A Simulation Study
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