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: (i) their practical applicability can be hindered by non-convex loss functions; and (ii) they may suffer from poor performance and instability due to inverse probability weighting and pseudo-outcomes that violate bounds. To overcome these issues, the EP-learner leverages an efficient plug-in estimator of the population risk function for the causal contrast. In doing so, it inherits the stability of plug-in strategies such as T-learning, while improving on their efficiency. 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 show that EP-learners of the conditional average treatment effect and conditional relative risk outperform state-of-the-art competitors, including the T-learner, R-learner, and DR-learner. Open-source implementations of the proposed methods are available in our \texttt{R} package \texttt{hte3}.

翻译：我们提出高效插件（EP）学习，这是一种用于估计异质性因果对比的新框架，例如条件平均处理效应和条件相对风险。EP学习框架享有与Neyman正交学习策略（如DR学习和R学习）相同的预言机效率，同时解决了它们的一些主要缺陷：（i）非凸损失函数可能阻碍其实际应用；（ii）由于逆概率加权和违反边界的伪结果，它们可能表现出较差的性能和稳定性。为克服这些问题，EP学习器利用因果对比的群体风险函数的高效插件估计量。通过这种方式，它继承了T学习等插件策略的稳定性，同时提高了其效率。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，在渐近意义上等价于群体风险函数的预言机高效一步去偏估计量的最小化器。在模拟实验中，我们展示了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对手，包括T学习器、R学习器和DR学习器。所提方法的开源实现可在我们的\texttt{R}包\texttt{hte3}中获取。