Efficiently and flexibly estimating treatment effect heterogeneity is an important task in a wide variety of settings ranging from medicine to marketing, and there are a considerable number of promising conditional average treatment effect estimators currently available. These, however, typically rely on the assumption that the measured covariates are enough to justify conditional exchangeability. We propose the P-learner, motivated by the R- and DR-learner, a tailored two-stage loss function for learning heterogeneous treatment effects in settings where exchangeability given observed covariates is an implausible assumption, and we wish to rely on proxy variables for causal inference. Our proposed estimator can be implemented by off-the-shelf loss-minimizing machine learning methods, which in the case of kernel regression satisfies an oracle bound on the estimated error as long as the nuisance components are estimated reasonably well.

翻译：高效且灵活地估计治疗效果的异质性是广泛应用于医学到市场营销等多个领域的重要任务，目前已有大量有前景的条件平均治疗效果估计器。然而，这些方法通常依赖于可测量协变量足以证明条件可交换性的假设。我们提出P-学习器，其灵感来源于R-和DR-学习器，是一种针对性的两阶段损失函数，用于在给定观测协变量可交换性是不合理假设的情形下学习异质治疗效果，并希望借助代理变量进行因果推断。我们所提出的估计器可通过现成的损失最小化机器学习方法实现，在核回归情况下，只要干扰分量估计得足够好，该估计器即可在估计误差上满足预言机界限。