Abstract
Constraint-based (CB) methods are widely used for discovering causal relationships in observational data. The PC-stable algorithm is a prominent example of CB methods. A critical component of the PC-stable algorithm is to find d-separators and perform conditional independence (CI) tests to eliminate spurious causal relationships. While the pairwise CI tests are necessary for identifying causal relationships, the error rate, where true causal relationships are erroneously removed, increases with the number of tests performed. Efficiently searching for the true d-separator set is thus a critical component to increase the accuracy of the causal graph. To this end, we propose a novel recursive algorithm for constructing causal graphs, based on a two-phase divide and conquer strategy. In phase one, we recursively partition the undirected graph using community detection, and subsequently construct partial skeletons from each partition. Phase two uses a bottom-up approach to merge the subgraph skeletons, ultimately yielding the full causal graph. Simulations on several real-world data sets show that our approach effectively finds the d-separators, leading to a significant improvement in the quality of causal graphs.
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Notes
- 1.
A moral graph is the same as a skeleton except it has edges between pairs of variables having a common child.
- 2.
In this work, we will refer to nodes and variables interchangeably.
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Acknowledgements
This material is based upon work supported by the NSF grant 1029711. In addition, this material is based on work supported in part by the DOE SDAVI Institute and the U.S. National Science Foundation (Expeditions in Computing program).
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Chaudhary, M.S., Ranshous, S., Samatova, N.F. (2018). A Community-Driven Graph Partitioning Method for Constraint-Based Causal Discovery. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_21
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