# Cost-Effective Incentive Allocation via Structured Counterfactual Inference

arXiv: https://arxiv.org/abs/1902.02495 · HTML: https://mlsanity.com/paper/1902.02495/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2019.

## Built on by (most cited first, 12 of 12)

- [E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modeling](https://mlsanity.com/paper/2108.13298/) (2021)
- [Model-based Constrained MDP for Budget Allocation in Sequential Incentive Marketing](https://mlsanity.com/paper/2303.01049/) (2023)
- [A Survey of Deep Causal Models and Their Industrial Applications](https://mlsanity.com/paper/2209.08860/) (2022)
- [End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling](https://mlsanity.com/paper/2408.11623/) (2024)
- [Enhancing Counterfactual Classification via Self-Training](https://mlsanity.com/paper/2112.04461/) (2021)
- [Off-Policy Evaluation with Policy-Dependent Optimization Response](https://mlsanity.com/paper/2202.12958/) (2022)
- [Identifying Influential Users in Unknown Social Networks for Adaptive Incentive Allocation Under Budget Restriction](https://mlsanity.com/paper/2107.05992/) (2021)
- [GAC: A Deep Reinforcement Learning Model Toward User Incentivization in Unknown Social Networks](https://mlsanity.com/paper/2203.09578/) (2022)
- [A Distributionally Robust Framework for Nuisance in Causal Effect Estimation](https://mlsanity.com/paper/2505.17717/) (2025)
- [Learning Treatment Representations for Downstream Instrumental Variable Regression](https://mlsanity.com/paper/2506.02200/) (2025)
- [Deployment of AI-Assisted Interventions: Capacity Constraints and Noisy Compliance](https://mlsanity.com/paper/2604.14370/) (2026)
- [Learning to Allocate Incentives for Incentivized Advertising via Offline Model-Based Reinforcement Learning](https://mlsanity.com/paper/2608.28065/) (2026)

## Abstract (arXiv)

We address a practical problem ubiquitous in modern marketing campaigns, in which a central agent tries to learn a policy for allocating strategic financial incentives to customers and observes only bandit feedback. In contrast to traditional policy optimization frameworks, we take into account the additional reward structure and budget constraints common in this setting, and develop a new two-step method for solving this constrained counterfactual policy optimization problem. Our method first casts the reward estimation problem as a domain adaptation problem with supplementary structure, and then subsequently uses the estimators for optimizing the policy with constraints. We also establish theoretical error bounds for our estimation procedure and we empirically show that the approach leads to significant improvement on both synthetic and real datasets.