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arXiv · 2607.14318

Counterfactual Optimal Action Trees (COAT): Interpretable Prescriptive Policies from Observational Data

Youssef Drissi·Markus Ettl·Shivaram Subramanian·Wei Sun·Zack Xue
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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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Youssef Drissi, Markus Ettl, Shivaram Subramanian, Wei Sun, Zack Xue. 2026-07-15. Counterfactual Optimal Action Trees (COAT): Interpretable Prescriptive Policies from Observational Data. https://arxiv.org/abs/2607.14318

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