摘要翻译：
基于观察到的个人数据来分配治疗越来越有兴趣：例子包括不同的定价、个性化的信贷提供和有针对性的社会项目。政策目标引入激励措施，鼓励个人改变自己的行为，以获得更好的治疗。我们证明了当观察到的协变量内生于治疗分配规则时，基于标准风险最小化的估计量是次优的。我们提出了一个动态实验，它收敛到最优治疗分配函数，而不需要对个体策略行为的参数假设，并证明了它具有以线性速率衰减的遗憾。我们在模拟和小型MTurk实验中验证了该方法。
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英文标题：
《Learning to Personalize Treatments When Agents Are Strategic》
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作者：
Evan Munro
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最新提交年份：
2021
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分类信息：

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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一级分类：Computer Science        计算机科学
二级分类：Computer Science and Game Theory        计算机科学与博弈论
分类描述：Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
涵盖计算机科学和博弈论交叉的所有理论和应用方面，包括机制设计的工作，游戏中的学习（可能与学习重叠），游戏中的agent建模的基础（可能与多agent系统重叠），非合作计算环境的协调、规范和形式化方法。该领域还涉及博弈论在电子商务等领域的应用。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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英文摘要：
  There is increasing interest in allocating treatments based on observed individual data: examples include heterogeneous pricing, individualized credit offers, and targeted social programs. Policy targeting introduces incentives for individuals to modify their behavior to obtain a better treatment. We show standard risk minimization-based estimators are sub-optimal when observed covariates are endogenous to the treatment allocation rule. We propose a dynamic experiment that converges to the optimal treatment allocation function without parametric assumptions on individual strategic behavior, and prove that it has regret that decays at a linear rate. We validate the method in simulations and in a small MTurk experiment.
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