Hybrid Causal-Non-Causal Pre-Training for Fair TabPFN Models

Abstract

Abstract: With the growing adoption of machine learning (ML) systems in areas like law enforcement, criminal justice, finance, hiring, and admissions, it is increasingly critical to guarantee the fairness of decisions assisted by ML. In this paper, we study the tradeoff between fairness and accuracy under the statistical notion of equalized odds. We present a new upper bound on the accuracy (that holds for any classifier), as a function of the fairness budget. In addition, our bounds also exhibit dependence on the underlying statistics of the data, labels and the sensitive group attributes. We validate

Research Question

Can a hybrid causal-non-causal pre-training framework for TabPFN achieve better fairness-accuracy trade-offs than purely causal models, as evaluated on biased tabular datasets using accuracy scores and fairness metrics?

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Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance