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This paper proposes a reformulation to apply tabular foundation models (TFMs) to discrete choice estimation, addressing the structural gap of row-independent assumptions. The best reformulation outperforms hierarchical Bayesian estimation by 8% in holdout log-likelihood and 3.6% in hit rate while running 16 times faster.
This paper from Airbnb combines economic modeling and causal inference to understand how guests respond to prices and how preferences vary, aiming to optimize pricing tools and personalization in the two-sided marketplace.