# Contextual Multi-Armed Bandits for Causal Marketing

arXiv: https://arxiv.org/abs/1810.01859 · HTML: https://mlsanity.com/paper/1810.01859/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2018.

## Built on by (most cited first, 17 of 17)

- [CausalPFN: Amortized Causal Effect Estimation via In-Context Learning](https://mlsanity.com/paper/2506.07918/) (2025)
- [Causal Bandits for Linear Structural Equation Models](https://mlsanity.com/paper/2208.12764/) (2022)
- [Counterfactual inference in sequential experiments](https://mlsanity.com/paper/2202.06891/) (2022)
- [Causal Bandits without Graph Learning](https://mlsanity.com/paper/2301.11401/) (2023)
- [Doubly robust nearest neighbors in factor models](https://mlsanity.com/paper/2211.14297/) (2022)
- [An Experimental Design Approach for Regret Minimization in Logistic Bandits](https://mlsanity.com/paper/2202.02407/) (2022)
- [Adaptive Experimental Design and Counterfactual Inference](https://mlsanity.com/paper/2210.14369/) (2022)
- [Neural Insights for Digital Marketing Content Design](https://mlsanity.com/paper/2302.01416/) (2023)
- [Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice](https://mlsanity.com/paper/2402.10870/) (2024)
- [Uplifting Bandits](https://mlsanity.com/paper/2206.04091/) (2022)
- [Active and Passive Causal Inference Learning](https://mlsanity.com/paper/2308.09248/) (2023)
- [Encrypted Linear Contextual Bandit](https://mlsanity.com/paper/2103.09927/) (2021)
- [Machine Learning Prescriptive Canvas for Optimizing Business Outcomes](https://mlsanity.com/paper/2206.10333/) (2022)
- [SmartChoices: Augmenting Software with Learned Implementations](https://mlsanity.com/paper/2304.13033/) (2023)
- [Bandit Profit-maximization for Targeted Marketing](https://mlsanity.com/paper/2403.01361/) (2024)
- [Enhancing Uplift Modeling in Multi-Treatment Marketing Campaigns: Leveraging Score Ranking and Calibration Techniques](https://mlsanity.com/paper/2408.13628/) (2024)
- [Budget-Constrained Causal Bandits: Bridging Uplift Modeling and Sequential Decision-Making](https://mlsanity.com/paper/2604.26169/) (2026)

## Abstract (arXiv)

This work explores the idea of a causal contextual multi-armed bandit approach to automated marketing, where we estimate and optimize the causal (incremental) effects. Focusing on causal effect leads to better return on investment (ROI) by targeting only the persuadable customers who wouldn't have taken the action organically. Our approach draws on strengths of causal inference, uplift modeling, and multi-armed bandits. It optimizes on causal treatment effects rather than pure outcome, and incorporates counterfactual generation within data collection. Following uplift modeling results, we optimize over the incremental business metric. Multi-armed bandit methods allow us to scale to multiple treatments and to perform off-policy policy evaluation on logged data. The Thompson sampling strategy in particular enables exploration of treatments on similar customer contexts and materialization of counterfactual outcomes. Preliminary offline experiments on a retail Fashion marketing dataset show merits of our proposal.