Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choice
📰 ArXiv cs.AI
Learn to audit and fix economic validity in tabular foundation models for discrete choice, ensuring predictions align with economic logic
Action Steps
- Estimate a standard choice model using the foundation model's predictions as inputs
- Embed the foundation model predictions within a utility-maximization framework using a two-stage adapter
- Audit the model's predictions for economic validity by checking for violations of economic logic
- Fix invalid predictions by adjusting the model's parameters or using alternative modeling approaches
- Evaluate the improved model's performance on choice prediction tasks
Who Needs to Know This
Data scientists and economists working with foundation models for choice prediction tasks can benefit from this approach to improve model validity and reliability
Key Insight
💡 Embedding foundation model predictions within a utility-maximization framework can help ensure economic validity and reliability
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📊 Improve economic validity in tabular foundation models for discrete choice with a two-stage adapter 📈
Key Takeaways
Learn to audit and fix economic validity in tabular foundation models for discrete choice, ensuring predictions align with economic logic
Full Article
Title: Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choice
Abstract:
arXiv:2605.26559v1 Announce Type: cross Abstract: 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 choic
Abstract:
arXiv:2605.26559v1 Announce Type: cross Abstract: 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 choic
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