Orthogonal meta-learners, such as DR-learner, R-learner and IF-learner, are increasingly used to estimate conditional average treatment effects. They improve convergence rates relative to na\"{\i}ve meta-learners (e.g., T-, S- and X-learner) through de-biasing procedures that involve applying standard learners to specifically transformed outcome data. This leads them to disregard the possibly constrained outcome space, which can be particularly problematic for dichotomous outcomes: these typically get transformed to values that are no longer constrained to the unit interval, making it difficult for standard learners to guarantee predictions within the unit interval. To address this, we construct orthogonal meta-learners for the prediction of counterfactual outcomes which respect the outcome space. As such, the obtained i-learner or imputation-learner is more generally expected to outperform existing learners, even when the outcome is unconstrained, as we confirm empirically in simulation studies and an analysis of critical care data. Our development also sheds broader light onto the construction of orthogonal learners for other estimands.

翻译：正交元学习器（如DR学习器、R学习器和IF学习器）越来越多地被用于估计条件平均处理效应。它们通过去偏程序（将标准学习器应用于经过特定转换的结果数据）相比朴素元学习器（如T学习器、S学习器和X学习器）提高了收敛速度。这导致它们可能忽略受约束的结果空间，对于二分结果尤其成问题：这些结果通常被转换为不再受限于单位间隔的值，使得标准学习器难以保证在单位间隔内的预测。为解决此问题，我们构建了尊重结果空间的正交元学习器来预测反事实结果。由此得到的i学习器或插补学习器在总体上预期能够优于现有学习器，即使结果不受约束时也是如此，这一点通过仿真研究和重症监护数据分析得到了实证确认。我们的研究也为其他估计量的正交学习器构建提供了更广泛的启示。