Generalization of LiNGAM that allows confounding
This is an incremental improvement for causal discovery in machine learning, addressing a specific bottleneck in LiNGAM methods.
The paper tackles the problem of LiNGAM's limitations with confounding by introducing LiNGAM-MMI, which quantifies confounding using KL divergence and optimizes variable order via a shortest path formulation, resulting in more accurate order determination in both confounding and non-confounding scenarios.
LiNGAM determines the variable order from cause to effect using additive noise models, but it faces challenges with confounding. Previous methods maintained LiNGAM's fundamental structure while trying to identify and address variables affected by confounding. As a result, these methods required significant computational resources regardless of the presence of confounding, and they did not ensure the detection of all confounding types. In contrast, this paper enhances LiNGAM by introducing LiNGAM-MMI, a method that quantifies the magnitude of confounding using KL divergence and arranges the variables to minimize its impact. This method efficiently achieves a globally optimal variable order through the shortest path problem formulation. LiNGAM-MMI processes data as efficiently as traditional LiNGAM in scenarios without confounding while effectively addressing confounding situations. Our experimental results suggest that LiNGAM-MMI more accurately determines the correct variable order, both in the presence and absence of confounding. The code is in the supplementary file in this link: https://github.com/SkyJoyTianle/ISIT2024.