What is the trade-off between pre-training computational cost (measured in FLOPs) and downstream fairness (mea

Abstract

Abstract: Foundation models for tabular data, such as the Tabular Prior-data Fitted Network (TabPFN), are pre-trained on a massive number of synthetic datasets generated by structural causal models (SCM). They leverage in-context learning to offer high predictive accuracy in real-world tasks. However, the fairness properties of these foundational models, which incorporate ideas from causal reasoning during pre-training, remain underexplored. In this work, we conduct a comprehensive empirical evaluation of TabPFN and its fine-tuned variants, assessing predictive performance, fairness, and robustness acro

Research Question

What is the trade-off between pre-training computational cost (measured in FLOPs) and downstream fairness (measured via demographic parity) when scaling the number of SCM features in TabPFN?

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