AI RESEARCH
Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choice
arXiv CS.AI
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ArXi:2605.26559v1 Announce Type: cross Tabular foundation models achieve strong accuracy on choice prediction tasks, but their predictions often violate the economic logic those tasks require: raising a price sometimes increases predicted demand, and implied willingness-to-pay estimates are frequently negative or implausible. We propose a two-stage adapter that embeds foundation model predictions within a utility-maximization framework. In the first stage, we estimate a standard choice model whose parameters are constrained to obey economic theory.