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

Researchers introduce COAT, a framework for learning interpretable prescriptive policies from observational data. They apply COAT to airline ancillary pricing, increasing upsell revenue per booking by 6.9% in a 17-week field pilot.

Why it matters

The development of COAT has implications for the field of artificial intelligence, particularly in the area of decision-making under uncertainty. The framework's ability to learn interpretable prescriptive policies from observational data has the potential to improve decision-making in various industries.

arxiv.org
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