用于离散选择估计的表格基础模型
Tabular Foundation Models for Discrete Choice Estimation
- Leeds School of Business, University of Colorado Boulder（科罗拉多大学博尔德分校利兹商学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究离散选择估计中表格基础模型的应用，提出编码选择集依赖性和个体异质性的重新表述方法，在酸奶扫描仪面板数据上评估，该方法在预测准确性和速度上优于分层贝叶斯估计，为基础模型应用于消费者选择问题提供原则性方法。
AI中文摘要：
表格基础模型（TFMs）通过上下文学习对结构化数据进行预测，无需特定任务估计。本文探讨TFMs能否有效应用于离散选择（营销和运营中的核心需求估计框架），发现直接应用效果有限。差距是结构性的，TFMs假设行独立观察，而离散选择本质上是集值的且存在消费者偏好异质性。提出一种在基于行的学习框架中编码选择集依赖性和个体异质性的重新表述。在酸奶扫描仪面板上评估，个体层面异质性编码是预测准确性的主要驱动因素。最佳重新表述在留出对数似然率上比分层贝叶斯估计高8%，命中率高3.6%，运行速度快16倍，在中等数据量（每位消费者10 - 40次购买场合）优势最大，对购买历史浅的消费者，在总体选择数据上微调有额外收益。这些结果为更广泛地将基础模型应用于消费者选择问题建立了原则性方法。
英文摘要：
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 heterogeneity encoding is the dominant driver of predictive accuracy. The best reformulation outperforms hierarchical Bayesian estimation on both holdout log-likelihood and hit rate, running 16 times faster, a practical advantage for large-scale demand estimation. The advantage is largest in the medium-data regime (10--40 purchase occasions per consumer), where parametric Bayesian shrinkage most distorts estimates for atypical consumers. Fine-tuning on population choice data provides additional gains for consumers with shallow purchase histories, where in-context learning has limited individual-specific signal to condition on. These results establish a principled approach for applying foundation models to consumer choice problems more broadly.
