Tabular Foundation Models for Discrete Choice Estimation
Tabular foundation models (TFMs) generate predictions on structured data via in-context learning, without task-specific estimation. We ask whether TFMs can be effectively applied to discrete choice, a central demand estimation framework in marketing and operations, and find that directly applying TFMs yields limited performance. The gap is structural: TFMs assume row-independent observations, whereas discrete choice is inherently set-valued and subject to persistent consumer preference heterogeneity. We propose a reformulation that encodes both choice-set dependence and individual heterogeneity within a row-based learning framework. Evaluated on a yogurt scanner panel, individual-level he...
| Authors | Liu Liu, Dan Zhang |
|---|---|
| Primary category | cs.LG |
| Published | 14 July 2026 |
| Last updated at source | 20 July 2026 |
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