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.

通过引入 LiNGAM-MMI 方法，该论文增强了 LiNGAM 模型，使用 KL 散度量化混淆的大小并按照最小化其影响的方式排列变量，实现全局最优的变量顺序，同时在有和无混淆的情况下以与传统 LiNGAM 相同的高效性处理数据。