Guardrailed Uplift Targeting: A Causal Optimization Playbook for Marketing Strategy

TL;DR

This paper presents a causal optimization framework for marketing that combines uplift modeling with business guardrails to improve targeting decisions, maximizing revenue and retention while respecting constraints.

Contribution

It introduces a novel decision framework that integrates heterogeneous treatment effect estimation with explicit business constraints for scalable marketing targeting.

Findings

Outperforms propensity and static baselines in offline simulations.

Consistently improves revenue and retention in online A/B tests.

Provides a reusable playbook for causal marketing targeting.

Abstract

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.