Rejoinder: New Objectives for Policy Learning

DOI10.1080/01621459.2020.1866580zbMath1464.62446arXiv2012.03130OpenAlexW3146927497MaRDI QIDQ4999146

Nathan Kallus

Publication date: 6 July 2021

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

Full work available at URL: https://arxiv.org/abs/2012.03130

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)

Cites Work

• Structural nested models and G-estimation: the partially realized promise
• Smooth discrimination analysis
• Data-driven robust optimization
• Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations
• Robust sample average approximation
• Performance guarantees for policy learning
• Semiparametric theory and missing data.
• Recovering Best Statistical Guarantees via the Empirical Divergence-Based Distributionally Robust Optimization