Unveil, model, and comprehend the causal mechanisms underpinning natural
phenomena stand as fundamental endeavors across myriad scientific disciplines.
Meanwhile, new knowledge emerges when discovering causal relationships from
data. Existing causal learning algorithms predominantly focus on the isolated
effects of variables, overlook the intricate interplay of multiple variables
and their collective behavioral patterns. Furthermore, the ubiquity of
high-dimensional data exacts a substantial temporal cost for causal algorithms.
In this paper, we develop a novel method called MgCSL (Multi-granularity Causal
Structure Learning), which first leverages sparse auto-encoder to explore
coarse-graining strategies and causal abstractions from micro-variables to
macro-ones. MgCSL then takes multi-granularity variables as inputs to train
multilayer perceptrons and to delve the causality between variables. To enhance
the efficacy on high-dimensional data, MgCSL introduces a simplified acyclicity
constraint to adeptly search the directed acyclic graph among variables.
Experimental results show that MgCSL outperforms competitive baselines, and
finds out explainable causal connections on fMRI datasets.

揭示、建模和理解自然现象的因果机制是科学研究中的基本任务，此文讨论一种名为 MgCSL（多粒度因果结构学习）的新方法，通过利用稀疏自动编码器探索从微观变量到宏观变量的粗粒度策略和因果抽象，使用多粒度变量作为输入训练多层感知机，从而深入研究变量之间的因果关系，并引入一种简化的非循环性约束以适应高维数据，实验证明 MgCSL 优于竞争方法，并在 fMRI 数据集中发现可解释的因果连接。