[2A4-GS-2] Machine learning

[2A4-GS-2-02] Improving DirectLiNGAM under small samples by estimating partial causal structure with independence evaluation

〇Shun Yanashima¹, Kento Uemura² (1. Tokyo Metropolitan University, 2. Fujitsu Limited)

Keywords:causal discovery, causal inference, LiNGAM, independence evaluation, small samples

In recent years, there have been various studies to estimate the causal relations of systems from observational samples. DirectLiNGAM is one of the popular causal discovery methods and aims at efficient and stable estimation by iteratively identifying and removing the variables at the top of the underlying causal relation. In real-world scenarios, sufficient samples are often not available due to technical, ethical, or cost problems. In such cases, DirectLiNGAM removes information necessary for estimating top variables as the iterations proceed, resulting in the deterioration of causal discovery performance. This paper proposes a new approach to address the problem by estimating the partial causal structures based on independent relations among variables and preserving the necessary information. We show that the proposed approach can reduce the estimation error by more than 80% compared to DirectLiNGAM on randomly generated causal discovery problems, especially under small samples.