Discussion of Kallus (2020) and Mo, Qi, and Liu (2020): New Objectives for Policy Learning

DOI10.1080/01621459.2020.1837140zbMath1464.62457OpenAlexW3149892790MaRDI QIDQ4999144

Alex Luedtke, Sijia Li, Xiudi Li

Publication date: 6 July 2021

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2020.1837140

zbMATH Keywords

optimisationefficient policy learningindividualized treatment regimes

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Causal inference from observational studies (62D20)

Uses Software

• grf

Cites Work

• Generalized random forests
• Estimation in semiparametric models. Some recent developments
• Unified methods for censored longitudinal data and causality
• Asymptotics for Statistical Treatment Rules
• Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

This page was built for publication: Discussion of Kallus (2020) and Mo, Qi, and Liu (2020): New Objectives for Policy Learning