Nonparametric Greedy Equivalence Search with Prior-Fitted Networks

Mateusz Gajewski, Mateusz Olko

Abstract:

Greedy equivalence search is among the most widely used methods for causal discovery. Recent work has established a theoretical foundation for extending GES to nonparametric models, an approach that relies on Bayesian likelihood estimation. In parallel, the prior–data fitted network paradigm was introduced, demonstrating superior accuracy and computational efficiency over standard tabular models across a wide range of predictive tasks, while naturally providing Bayesian predictive posteriors.
In this paper, we integrate TabPFN as a Bayesian likelihood estimator within nonparametric GES and conduct an extensive empirical evaluation of the resulting approach. The proposed method consistently outperforms state-of-the-art nonparametric causal discovery methods on a range of synthetic, simulated, and real-world datasets. These results highlight the PFN paradigm as a natural and promising direction for advancing causal discovery in complex real-world applications.