A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework serves as a basis of routine experimental analyses conducted by today's scientists across disciplines. In this paper, we demonstrate that Neyman's methodology can also be used to experimentally evaluate the efficacy of individualized treatment rules (ITRs), which are derived by modern causal machine learning algorithms. In particular, we show how to account for additional uncertainty resulting from a training process based on cross-fitting. The primary advantage of Neyman's approach is that it can be applied to any ITR regardless of the properties of machine learning algorithms that are used to derive the ITR. We also show, somewhat surprisingly, that for certain metrics, it is more efficient to conduct this ex-post experimental evaluation of an ITR than to conduct an ex-ante experimental evaluation that randomly assigns some units to the ITR. Our analysis demonstrates that Neyman's repeated sampling framework is as relevant for causal inference today as it has been since its inception.

本文展示了如何利用Neyman的方法通过实验证明个体化治疗规则(ITRs)的有效性，并解释了如何考虑交叉适配所引起的额外不确定性。该方法的主要优势在于，它适用于任何ITR，不受用于推导ITR的机器学习算法特性的限制，并表明对于某些度量标准，进行ITR的后期实验证明比进行随机分配一些单位至ITR的前期实验证明更为高效。通过分析，证明了Neyman的重复抽样框架在因果推断中是与其创建以来一样相关的。

尼曼遇见因果机器学习：个体化治疗规则的实验评估