Counterfactual Optimal Action Trees (COAT): Interpretable Prescriptive Policies from Observational Data
Youssef Drissi, Markus Ettl, Shivaram Subramanian, Wei Sun, Zack Xue
Abstract
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.
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Classified with taxonomy v2 on Sat, 5 Sept 2026.