Causal structure learning algorithms have focused almost exclusively on learning in “stable” environments in which the underlying causal structure does not change. Such changes often occur, however, without warning or signal in real-world environments. In this paper, we present DOCL, a novel causal structure learning algorithm that processes data in a dynamic, real-time manner and tracks changes in the generating causal structure or parameters. The algorithm learns in an online fashion from sequential or ordered data rather than “batch-mode” from a full dataset, and so supports causal learning in memory-limited settings. We show by simulation that the algorithm performs comparably to batch-mode learning when the causal structure is stationary, and significantly better in non-stationary environments.

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