Variable selection in double/debiased machine learning for causal inference: an outcome-adaptive approach

Autor:	Daijiro Kabata, Mototsugu Shintani
Rok vydání:	2021
Předmět:	Statistics and Probability Clustering high-dimensional data business.industry Estimator Feature selection Machine learning computer.software_genre Outcome (game theory) Modeling and Simulation Causal inference Treatment effect Artificial intelligence business computer Mathematics
Zdroj:	Communications in Statistics - Simulation and Computation. :1-14
ISSN:	1532-4141 0361-0918
DOI:	10.1080/03610918.2021.2001655
Popis:	Access to high-dimensional data has made the use of machine learning in causal inference more common in recent years. The double/debiased machine learning (DML) estimator for the treatment effect i...
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3bb0e8f87bb89e0c191b8bb44efddaa9 https://doi.org/10.1080/03610918.2021.2001655 Zobrazit plný text záznamu