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Guardrailed Uplift Targeting: A Causal Optimization Playbook for Marketing Strategy

Deepit Sapru

TL;DR

The paper tackles optimizing marketing targeting under business guardrails by combining CATE-based uplift estimation with constrained optimization to allocate treatments. It proposes a two-stage approach—estimate heterogeneous treatment effects with uplift models, then solve a constrained assignment problem under budget and experience constraints—validated offline with uplift AUC, IPS, and SNIPS, and online through large-scale A/B tests. Across retention, revenue maximization, and spend-threshold tasks, the framework yields consistent improvements while respecting guardrails, demonstrating a practical, scalable playbook for causal targeting. This work advances marketing personalization by integrating causal inference with explicit operational constraints, enabling revenue and retention improvements without compromising customer experience.

Abstract

This paper introduces a marketing decision framework that converts heterogeneous-treatment uplift into constrained targeting strategies to maximize revenue and retention while honoring business guardrails. The approach estimates Conditional Average Treatment Effects (CATE) with uplift learners and then solves a constrained allocation to decide who to target and which offer to deploy under limits such as budget or acceptable sales deterioration. Applied to retention messaging, event rewards, and spend-threshold assignment, the framework consistently outperforms propensity and static baselines in offline evaluations using uplift AUC, Inverse Propensity Scoring (IPS), and Self-Normalized IPS (SNIPS). A production-scale online A/B test further validates strategic lift on revenue and completion while preserving customer-experience constraints. The result is a reusable playbook for marketers to operationalize causal targeting at scale, set guardrails, and align campaigns with strategic KPIs.

Guardrailed Uplift Targeting: A Causal Optimization Playbook for Marketing Strategy

TL;DR

The paper tackles optimizing marketing targeting under business guardrails by combining CATE-based uplift estimation with constrained optimization to allocate treatments. It proposes a two-stage approach—estimate heterogeneous treatment effects with uplift models, then solve a constrained assignment problem under budget and experience constraints—validated offline with uplift AUC, IPS, and SNIPS, and online through large-scale A/B tests. Across retention, revenue maximization, and spend-threshold tasks, the framework yields consistent improvements while respecting guardrails, demonstrating a practical, scalable playbook for causal targeting. This work advances marketing personalization by integrating causal inference with explicit operational constraints, enabling revenue and retention improvements without compromising customer experience.

Abstract

This paper introduces a marketing decision framework that converts heterogeneous-treatment uplift into constrained targeting strategies to maximize revenue and retention while honoring business guardrails. The approach estimates Conditional Average Treatment Effects (CATE) with uplift learners and then solves a constrained allocation to decide who to target and which offer to deploy under limits such as budget or acceptable sales deterioration. Applied to retention messaging, event rewards, and spend-threshold assignment, the framework consistently outperforms propensity and static baselines in offline evaluations using uplift AUC, Inverse Propensity Scoring (IPS), and Self-Normalized IPS (SNIPS). A production-scale online A/B test further validates strategic lift on revenue and completion while preserving customer-experience constraints. The result is a reusable playbook for marketers to operationalize causal targeting at scale, set guardrails, and align campaigns with strategic KPIs.
Paper Structure (30 sections, 6 equations, 2 figures, 3 tables)

This paper contains 30 sections, 6 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodological Framework
  4. Problem Formulation
  5. Uplift Estimation Stage
  6. Constrained Optimization Stage
  7. Evaluation Methodology
  8. Experimental Applications and Results
  9. Customer Retention Optimization
  10. Business Context and Challenge
  11. Experimental Design
  12. Results and Analysis
  13. Event Revenue Maximization
  14. Business Context and Challenge
  15. Methodology and Constraints
  16. ...and 15 more sections

Figures (2)

  • Figure 1: Two-stage framework for guardrailed uplift targeting
  • Figure 2: Uplift distribution across customer segments showing heterogeneous treatment effects