Separability Analysis for Causal Discovery in Mixture of DAGs

Burak Varici, Dmitriy Katz, Dennis Wei, Prasanna Sattigeri, Ali Tajer

Abstract: Directed acyclic graphs (DAGs) are effective for compactly representing causal systems and specifying the causal relationships among the system's constituents. Specifying such causal relationships in some systems requires a mixture of multiple DAGs -- a single DAG is insufficient. Some examples include time-varying causal systems or aggregated subgroups of a population. Recovering the causal structure of the systems represented by single DAGs is investigated extensively, but it remains mainly open for the systems represented by a mixture of DAGs. A major difference between single- versus mixture-DAG recovery is the existence of node pairs that are separable in the individual DAGs but become inseparable in their mixture. This paper provides the theoretical foundations for analyzing such inseparable node pairs. Specifically, the notion of \emph{emergent edges} is introduced to represent such inseparable pairs that do not exist in the single DAGs but emerge in their mixtures. Necessary conditions for identifying the emergent edges are established. Operationally, these conditions serve as sufficient conditions for separating a pair of nodes in the mixture of DAGs. These results are further extended, and matching necessary and sufficient conditions for identifying the emergent edges in tree-structured DAGs are established. Finally, a novel graphical representation is formalized to specify these conditions, and an algorithm is provided for inferring the learnable causal relations.

Submission Length: Regular submission (no more than 12 pages of main content)

Changes Since Last Submission: Camera-ready version.

Code: https://github.com/bvarici/TMLR-mixture-DAG

Assigned Action Editor: ~Patrick_Flaherty1

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Submission Number: 1646