Fast Flow Matching based Conditional Independence Tests for Causal Discovery

TL;DR

This paper introduces FMCIT, a flow matching-based conditional independence test that significantly accelerates causal discovery by reducing computational costs while maintaining high accuracy, especially in high-dimensional settings.

Contribution

The paper presents FMCIT, a novel, efficient CI test leveraging flow matching, and integrates it into a two-stage causal discovery framework to improve speed and accuracy.

Findings

FMCIT effectively controls type-I error and maintains high power.

The integrated GPC-FMCIT framework reduces CI queries while preserving accuracy.

Experiments show superior efficiency and accuracy over existing methods.

Abstract

Constraint-based causal discovery methods require a large number of conditional independence (CI) tests, which severely limits their practical applicability due to high computational complexity. Therefore, it is crucial to design an algorithm that accelerates each individual test. To this end, we propose the Flow Matching-based Conditional Independence Test (FMCIT). The proposed test leverages the high computational efficiency of flow matching and requires the model to be trained only once throughout the entire causal discovery procedure, substantially accelerating causal discovery. According to numerical experiments, FMCIT effectively controls type-I error and maintains high testing power under the alternative hypothesis, even in the presence of high-dimensional conditioning sets. In addition, we further integrate FMCIT into a two-stage guided PC skeleton learning framework, termed GPC-FMCIT, which combines fast screening with guided, budgeted refinement using FMCIT. This design yields explicit bounds on the number of CI queries while maintaining high statistical power. Experiments on synthetic and real-world causal discovery tasks demonstrate favorable accuracy-efficiency trade-offs over existing CI testing methods and PC variants.