This paper introduces a marketing decision framework that optimizes customer targeting by integrating heterogeneous treatment effect estimation with explicit business guardrails. The objective is to maximize revenue and retention while adhering to constraints such as budget, revenue protection, and customer experience. The framework first estimates Conditional Average Treatment Effects (CATE) using uplift learners, then solves a constrained allocation problem to decide whom to target and which offer to deploy. It supports decisions in retention messaging, event rewards, and spend-threshold assignment. Validated through offline simulations and online A/B tests, the approach consistently outperforms propensity and static baselines, offering a reusable playbook for causal targeting at scale.

翻译：本文提出一种营销决策框架，通过将异质性处理效应估计与显式业务护栏相结合来优化客户目标定位。该框架旨在最大化收入与客户留存率，同时满足预算、收入保护及客户体验等约束条件。框架首先使用提升学习器估计条件平均处理效应（CATE），随后通过求解约束分配问题来决定目标客户群体及应部署的营销方案。该框架支持留存信息推送、活动奖励发放及消费阈值设定等多类决策场景。经离线仿真与在线A/B测试验证，本方法在各项指标上均持续优于倾向性评分模型及静态基线策略，为规模化因果目标定位提供了可复用的操作指南。