Structure Learning in Causal Cyclic Networks


Sleiman Itani, Mesrob Ohannessian, Karen Sachs, Garry P. Nolan, Munther A. Dahleh ;
Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:165-176, 2010.


Cyclic graphical models are unnecessary for accurate representation of joint probability distributions, but are often indispensable when a causal representation of variable relationships is desired. For variables with a cyclic causal dependence structure, DAGs are guaranteed not to recover the correct causal structure, and therefore may yield false predictions about the outcomes of perturbations (and even inference.) In this paper, we introduce an approach to generalize Bayesian Network structure learning to structures with cyclic dependence. We introduce a structure learning algorithm, prove its performance given reasonable assumptions, and use simulated data to compare its results to the results of standard Bayesian network structure learning. We then propose a modified, heuristic algorithm with more modest data requirements, and test its performance on a real-life dataset from molecular biology, containing causal, cyclic dependencies.

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