Many decision-making problems require ranking individuals by their treatment effects rather than estimating the exact effect magnitudes. Examples include prioritizing patients for preventive care interventions, or ranking customers by the expected incremental impact of an advertisement. Surprisingly, while causal effect estimation has received substantial attention in the literature, the problem of directly learning rankings of treatment effects has largely remained unexplored. In this paper, we introduce Rank-Learner, a novel two-stage learner that directly learns the ranking of treatment effects from observational data. We first show that naive approaches based on precise treatment effect estimation solve a harder problem than necessary for ranking, while our Rank-Learner optimizes a pairwise learning objective that recovers the true treatment effect ordering, without explicit CATE estimation. We further show that our Rank-Learner is Neyman-orthogonal and thus comes with strong theoretical guarantees, including robustness to estimation errors in the nuisance functions. In addition, our Rank-Learner is model-agnostic, and can be instantiated with arbitrary machine learning models (e.g., neural networks). We demonstrate the effectiveness of our method through extensive experiments where Rank-Learner consistently outperforms standard CATE estimators and non-orthogonal ranking methods. Overall, we provide practitioners with a new, orthogonal two-stage learner for ranking individuals by their treatment effects.

翻译：许多决策问题要求根据个体的处理效应进行排序，而非精确估计效应大小。例如，在预防性护理干预中优先考虑患者，或根据广告的预期增量影响对客户进行排序。值得注意的是，尽管因果效应估计在文献中已受到广泛关注，但直接学习处理效应排序的问题在很大程度上仍未得到探索。本文提出Rank-Learner，一种新颖的两阶段学习器，可直接从观测数据中学习处理效应的排序。我们首先证明，基于精确处理效应估计的朴素方法为解决排序问题求解了比必要更困难的子问题，而我们的Rank-Learner通过优化成对学习目标来恢复真实的处理效应顺序，无需显式进行条件平均处理效应（CATE）估计。我们进一步证明Rank-Learner具有Neyman正交性，因此具备强大的理论保证，包括对辅助函数估计误差的鲁棒性。此外，Rank-Learner是模型无关的，可通过任意机器学习模型（如神经网络）进行实例化。我们通过大量实验证明了该方法的有效性，其中Rank-Learner始终优于标准的CATE估计器与非正交排序方法。总体而言，我们为实践者提供了一种全新的正交两阶段学习器，用于根据处理效应对个体进行排序。