BCCB unifies learning of heterogeneous ad responses, exploration of uncertain users, and budget pacing into a single online process that works effectively from the first user on the Criteo Uplift dataset.
Contextual Multi-Armed Bandits for Causal Marketing
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
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.
verdicts
UNVERDICTED 2representative citing papers
A review that organizes causal decision making into three stages and consolidates methods into an open Python collection.
citing papers explorer
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Budget-Constrained Causal Bandits: Bridging Uplift Modeling and Sequential Decision-Making
BCCB unifies learning of heterogeneous ad responses, exploration of uncertain users, and budget pacing into a single online process that works effectively from the first user on the Criteo Uplift dataset.
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A Review of Causal Decision Making
A review that organizes causal decision making into three stages and consolidates methods into an open Python collection.