Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules

DOI10.1080/01621459.2020.1866581zbMath1464.62468arXiv2110.08936OpenAlexW3205263583MaRDI QIDQ4999147

Weibin Mo, Yu Feng Liu, Zhengling Qi

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/2110.08936

zbMATH Keywords

generalizabilitydistributionally robust optimizationindividualized treatment rulescovariate shifts

Mathematics Subject Classification ID

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

Cites Work

• External validity: from do-calculus to transportability across populations
• Statistical analysis of kernel-based least-squares density-ratio estimation
• Covariate balancing propensity score by tailored loss functions
• Invariance, causality and robustness
• Robustifying trial-derived optimal treatment rules for a target population
• Semiparametric theory and missing data.
• Dealing with limited overlap in estimation of average treatment effects
• Robust Estimation of Encouragement Design Intervention Effects Transported Across Sites
• Policy Learning With Observational Data
• Double/debiased machine learning for treatment and structural parameters
• Prediction, Estimation, and Attribution
• Estimation of Individualized Decision Rules Based on an Optimized Covariate-Dependent Equivalent of Random Outcomes
• Multiple robustness in factorized likelihood models
• Covariate Balancing Propensity Score
• Veridical data science

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