Tabular Foundation Models for Discrete Choice Estimation
Researchers reformulate tabular foundation models (TFMs) to address structural challenges in discrete choice estimation, such as choice-set dependence and consumer heterogeneity. Their approach encodes these factors within a row-based learning framework and, when evaluated on a yogurt scanner panel, outperforms hierarchical Bayesian estimation by 8% in holdout log-likelihood and 3.6% in hit rate, while being 16 times faster. The method is particularly effective in medium-data regimes (10–40 purchase occasions per consumer), where traditional Bayesian methods can distort estimates for atypical consumers.
Why it matters: This work demonstrates a significant advance in applying foundation models to consumer choice estimation, offering both improved predictive performance and substantial computational speedups over established methods.
Full story at: arXiv Machine Learning ↗