We define a prescriptive analytics framework that addresses the needs of a constrained decision-maker facing, ex ante, unknown costs and benefits of multiple policy levers. The framework is general in nature and can be deployed in any utility-maximizing context, public or private. It relies on randomized field experiments for causal inference, machine learning for estimating heterogeneous treatment effects, and on the optimization of an integer linear program for converting predictions into decisions. The net result is the discovery of individual-level targeting of policy interventions to maximize overall utility under a budget constraint. The framework is set in the context of the four pillars of analytics and is especially valuable for companies that already have an existing practice of running A/B tests. The key contribution of this work is to develop and operationalize a framework to exploit both within- and between-treatment arm heterogeneity in the utility response function in order to derive benefits from future (optimized) prescriptions. We demonstrate the value of this framework as compared to benchmark practices—i.e., the use of the average treatment effect, uplift modeling, as well as an extension to contextual bandits—in two different settings. Unlike these standard approaches, our framework is able to recognize, adapt to, and exploit the (potential) presence of different subpopulations that experience varying costs and benefits within a treatment arm while also exhibiting differential costs and benefits across treatment arms. As a result, we find a targeting strategy that produces an order of magnitude improvement in expected total utility for the case where significant within- and between-treatment arm heterogeneity exists.

中文翻译：

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使用异构治疗效果优化策略部署的规范性分析框架

我们定义了一个规范性分析框架，以满足受限决策者的需求，这些决策者在事前面临多种政策杠杆的未知成本和收益。该框架本质上是通用的，可以部署在任何效用最大化的环境中，无论是公共的还是私有的。它依赖于因果推理的随机现场实验、估计异质治疗效果的机器学习以及将预测转化为决策的整数线性程序的优化。最终结果是发现了针对个人层面的政策干预目标，以在预算约束下最大化整体效用。该框架是在分析的四大支柱的背景下设置的，对于已经拥有运行 A/B 测试的现有实践的公司尤其有价值。这项工作的主要贡献是开发和实施一个框架，以利用效用响应函数中治疗组内和治疗组之间的异质性，以便从未来（优化的）处方中获益。与基准实践相比，我们展示了该框架的价值——即在两种不同的设置中使用平均治疗效果、提升建模以及对上下文强盗的扩展。与这些标准方法不同，我们的框架能够识别、适应和利用不同亚群的（潜在）存在，这些亚群在治疗组内经历不同的成本和收益，同时还表现出不同治疗组的成本和收益差异。因此，