Contextual Multi-Armed Bandits for Causal Marketing
This work addresses automated marketing for businesses by focusing on causal effects to improve return on investment, though it appears incremental as it combines existing techniques from causal inference, uplift modeling, and multi-armed bandits.
The paper tackles the problem of optimizing marketing ROI by targeting persuadable customers using a causal contextual multi-armed bandit approach, with preliminary offline experiments on a retail Fashion dataset showing its merits.
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.