Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning
利用因果图分区在高维结构化假设空间中发现因果关系
The aim in many sciences is to understand the mechanisms that underlie the observed distribution of variables, starting from a set of initial hypotheses. Causal discovery allows us to infer mechanisms as sets of cause and effect relationships in a generalized way -- without necessarily tailoring to a specific domain. Causal discovery algorithms search over a structured hypothesis space, defined by the set of directed acyclic graphs, to find the graph that best explains the…