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The paper introduces COAT (Counterfactual Optimal Action Tree), a novel framework that learns interpretable prescriptive policies from observational data by integrating counterfactual outcome estimation with large-scale mixed-integer optimization. This approach was applied to airline ancillary pricing, where it successfully navigated complex business rules to enhance decision-making under constraints. In a 17-week field pilot, COAT achieved a 6.9% increase in upsell revenue per booking, translating to an estimated \$50-\$150 million in incremental annual revenue for the airline.
COAT's innovative blend of counterfactual reasoning and optimization led to a significant revenue boost, showcasing the power of interpretable AI in real-world applications.
We introduce COAT (Counterfactual Optimal Action Tree), a framework for learning interpretable prescriptive policies from observational data. COAT combines counterfactual outcome estimation with large-scale mixed-integer optimization, using column generation to translate causal predictions into feasible, transparent decisions under business and regulatory constraints. We apply COAT to airline ancillary pricing, a setting characterized by complex business rules and limited experimental flexibility. In a 17-week field pilot with a major global airline, COAT increased upsell revenue per booking by 6.9%, with the airline projecting \$50-\$150 million in incremental annual premium seat revenue across eligible domestic markets. The success of the pilot led to scaled adoption and informed broader AI-driven decision initiatives within the organization.