Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine
Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine
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动态治疗方案的统计方法：强化学习、因果推理和个性化医疗
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Bibhas Chakraborty
中科院分区：
文献类型：
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作者：
Bibhas Chakraborty
介绍。数据：观察性研究和序列随机试验。统计强化学习通过对条件平均结果的对比进行建模来估计最佳DTR。通过直接建模方案估计最佳DTR。G计算：最佳DTR的参数估计。替代结果类型的估计DTR。推理和非规则性。最后的思考和思考-词汇表索引。-参考资料。
Introduction.- The Data: Observational Studies and Sequentially Randomized Trials.- Statistical Reinforcement Learning.- Estimation of Optimal DTRs by Modeling Contrasts of Conditional Mean Outcomes.- Estimation of Optimal DTRs by Directly Modeling Regimes.- G-computation: Parametric Estimation of Optimal DTRs.- Estimation DTRs for Alternative Outcome Types.- Inference and Non-regularity.- Additional Considerations and Final Thoughts.- Glossary.- Index.- References.