摘要翻译：
在治疗分配问题中，被治疗的个体往往是按顺序到达的。我们研究了这样一个问题：决策者不仅对预期的累积福利感兴趣，而且还关心治疗结果的不确定性/风险。一开始，要做的治疗任务总数甚至可能是未知的。提出了一种达到极大极小最优遗憾的序贯处理策略。我们还证明了期望的次优处理数只在处理数中缓慢增长。最后，我们研究了一个结果只被延迟观察的环境。
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英文标题：
《Optimal sequential treatment allocation》
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作者：
Anders Bredahl Kock and Martin Thyrsgaard
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最新提交年份：
2018
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分类信息：

一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  In treatment allocation problems the individuals to be treated often arrive sequentially. We study a problem in which the policy maker is not only interested in the expected cumulative welfare but is also concerned about the uncertainty/risk of the treatment outcomes. At the outset, the total number of treatment assignments to be made may even be unknown. A sequential treatment policy which attains the minimax optimal regret is proposed. We also demonstrate that the expected number of suboptimal treatments only grows slowly in the number of treatments. Finally, we study a setting where outcomes are only observed with delay.
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PDF链接：
https://arxiv.org/pdf/1705.09952