  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.
