对比四种方法在百万级营销数据上估个体增益效果，S-Learner表现最优。
A Large-Scale Empirical Comparison of Meta-Learners and Causal Forests for Heterogeneous Treatment Effect Estimation in Marketing Uplift Modeling
- 用LightGBM基模型比较S/T/X-Learner和因果森林四类方法
- S-Learner Qini达0.376，前20%用户贡献77.7%增量转化
- 发现特征f8是主要影响因素，可识别出1.9%高信心说服对象
估计个体层面的条件平均处理效应（CATE）是精准营销的核心，但工业级大规模方法对比仍不足。我们构建了UpliftBench，基于包含1398万客户记录的Criteo Uplift v2.1数据集，对四种CATE估计器进行实证评估：以LightGBM为基模型的S-Learner、T-Learner、X-Learner，以及使用EconML实现的Causal Forest。近随机的处理分配（倾向得分AUC=0.509）保障了因果推断的内部有效性。通过Qini系数和累积增益曲线评估，S-Learner取得最高Qini值0.376，按预测CATE排序的前20%客户捕捉了77.7%的增量转化，较随机投放提升3.9倍。SHAP分析显示，在12个匿名协变量中，特征f8是主导的异质处理效应驱动因子。因果森林的不确定性量化揭示，1.9%的客户为高置信度说服对象（95%置信下界>0），0.1%为高置信度沉睡群体（95%置信上界<0）。研究结果为大规模提升建模流程的方法选择提供了实证依据。
原文摘要 · Abstract (English)
Estimating Conditional Average Treatment Effects (CATE) at the individual level is central to precision marketing, yet systematic benchmarking of uplift modeling methods at industrial scale remains limited. We present UpliftBench, an empirical evaluation of four CATE estimators: S-Learner, T-Learner, X-Learner (all with LightGBM base learners), and Causal Forest (EconML), applied to the Criteo Uplift v2.1 dataset comprising 13.98 million customer records. The near-random treatment assignment (propensity AUC = 0.509) provides strong internal validity for causal estimation. Evaluated via Qini coefficient and cumulative gain curves, the S-Learner achieves the highest Qini score of 0.376, with the top 20% of customers ranked by predicted CATE capturing 77.7% of all incremental conversions, a 3.9x improvement over random targeting. SHAP analysis identifies f8 as the dominant heterogeneous treatment effect (HTE) driver among the 12 anonymized covariates. Causal Forest uncertainty quantification reveals that 1.9% of customers are confident persuadables (lower 95% CI > 0) and 0.1% are confident sleeping dogs (upper 95% CI < 0). Our results provide practitioners with evidence-based guidance on method selection for large-scale uplift modeling pipelines.
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