In many areas, practitioners seek to use observational data to learn a treatment assignment policy that satisfies application‐specific constraints, such as budget, fairness, simplicity, or other functional form constraints. For example, policies may be restricted to take the form of decision trees based on a limited set of easily observable individual characteristics. We propose a new approach to this problem motivated by the theory of semiparametrically efficient estimation. Our method can be used to optimize either binary treatments or infinitesimal nudges to continuous treatments, and can leverage observational data where causal effects are identified using a variety of strategies, including selection on observables and instrumental variables. Given a doubly robust estimator of the causal effect of assigning everyone to treatment, we develop an algorithm for choosing whom to treat, and establish strong guarantees for the asymptotic utilitarian regret of the resulting policy.

中文翻译：

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通过观察数据进行政策学习

在许多领域，从业者寻求使用观察数据来学习满足特定应用约束的治疗分配策略，例如预算，公平，简单或其他功能形式约束。例如，基于一组有限的易于观察的个体特征，可以将策略限制为采用决策树的形式。我们提出了一种基于半参数有效估计理论的针对此问题的新方法。我们的方法可用于优化二元处理或无穷微移至连续处理，并可以利用观测数据，其中使用各种策略（包括对可观察变量和工具变量的选择）来确定因果关系。假设将所有人分配给治疗的因果关系加倍可靠，