Profit-Aligned CATE Estimation: Reconciling Policy Learning and Inference
与利润目标一致的条件平均处理效应估计：协调政策学习与推断
We propose a framework that aligns Conditional Average Treatment Effect (CATE) estimation with profit maximization. Our method recognizes that, for customers with extreme treatment effects, additional estimation accuracy is unlikely to change the recommended actions. In contrast, accuracy is critical near the decision boundary, where treatment effects are close to treatment costs. Our approach optimizes a novel objective function that concentrates learning capacity along…