We introduce efficient plug-in (EP) learning, a novel framework for the estimation of heterogeneous causal contrasts, such as the conditional average treatment effect and conditional relative risk. The EP-learning framework enjoys the same oracle-efficiency as Neyman-orthogonal learning strategies, such as DR-learning and R-learning, while addressing some of their primary drawbacks, including that (i) their practical applicability can be hindered by loss function non-convexity; and (ii) they may suffer from poor performance and instability due to inverse probability weighting and pseudo-outcomes that violate bounds. To avoid these drawbacks, EP-learner constructs an efficient plug-in estimator of the population risk function for the causal contrast, thereby inheriting the stability and robustness properties of plug-in estimation strategies like T-learning. Under reasonable conditions, EP-learners based on empirical risk minimization are oracle-efficient, exhibiting asymptotic equivalence to the minimizer of an oracle-efficient one-step debiased estimator of the population risk function. In simulation experiments, we illustrate that EP-learners of the conditional average treatment effect and conditional relative risk outperform state-of-the-art competitors, including T-learner, R-learner, and DR-learner. Open-source implementations of the proposed methods are available in our R package hte3.

翻译：我们提出高效插件（EP）学习，这是一种用于异质性因果对比估计的新框架，适用于条件平均处理效应和条件相对风险等场景。EP学习框架在保持Neyman正交学习策略（如DR学习和R学习）的预言机高效性的同时，解决了其主要缺陷，包括：(i) 损失函数非凸性可能限制其实际应用性；(ii) 由于逆概率加权和违反界限的伪结果，可能导致性能不佳和稳定性差。为避免这些缺陷，EP学习器为因果对比的总体风险函数构建高效插件估计量，从而继承T学习等插件估计策略的稳定性和鲁棒性。在合理条件下，基于经验风险最小化的EP学习器具有预言机高效性，其渐近等价于总体风险函数的预言机高效一步去偏估计量的最小化器。仿真实验中，我们证明条件平均处理效应和条件相对风险的EP学习器优于最先进的竞争方法，包括T学习器、R学习器和DR学习器。所提出方法的开源实现已收录于我们的R包hte3中。