提出新方法精准估计比率型治疗效应，适用于医疗定价等场景。
Beyond Differences: Doubly Robust Meta-Learners for Ratio-Based Treatment Effects
- 将比率型因果效应分解为两个倾向得分分类任务，简化计算
- 在低转化率场景下表现最优，避免传统方法的不平衡问题
- 在有混杂因素的观察数据中显著领先，适合实际应用
当治疗效应以比率形式自然呈现时（如医学、定价、营销），比率型条件平均处理效应 τ(x) = E[Y|W=1,X=x] / E[Y|W=0,X=x] 是合适的估计目标。现有方法要么强加对数线性参数结构，要么使用通用回归但缺乏稳健性保证。本文提出 Q-Learner，将 τ(x) 分解为两个几率比的乘积，使二元结果下的比率-CATE估计转化为两个倾向得分分类任务。进一步推导出 S/T- 和 Q 风格比率学习器的双重稳健增强版本，并刻画其不同的稳健性特性。在七个随机对照试验（RCT）数据集上的基准测试显示，Q-Learner 在低转化率情形下表现最稳定，因其仅依赖倾向得分的构造规避了影响结果估计的不平衡回归问题。在四个观察性数据集上，由于需估计倾向得分且混杂无法排除，本文提出的双重稳健学习器显著胜出，成为有混杂的观察数据下从业者首选方法。
原文摘要 · Abstract (English)
When treatment effects are naturally expressed as ratios -- as in medicine, pricing, and marketing -- the ratio-based CATE $τ(x) = E[Y|W=1,X=x] / E[Y|W=0,X=x]$ is the appropriate estimand. Yet existing estimators either impose a log-linear parametric structure or apply generic regression without robustness guarantees for this functional. We introduce the Q-Learner, which decomposes $τ(x)$ into a product of two odds ratios, reducing ratio-CATE estimation for binary outcomes to two propensity classification tasks. We further derive doubly robust augmentations for both S/T- and Q-style ratio learners and characterize their distinct robustness properties. In benchmarks on seven RCT datasets, the Q-Learner is the most consistently competitive method in low-conversion regimes, where its propensity-only construction sidesteps the imbalanced regression that hurts outcome-based estimators. On four observational datasets, where propensity must be estimated and confounding cannot be ruled out, the DR learners introduced here decisively come out on top, making them practitioners' natural default for confounded observational data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。