Causal Discovery from Changes

Abstract

We propose a new method of discovering causal structures, based on the detection of local, spontaneous changes in the underlying data-generating model. We analyze the classes of structures that are equivalent relative to a stream of distributions produced by local changes, and devise algorithms that output graphical representations of these equivalence classes. We present experimental results, using simulated data, and examine the errors associated with detection of changes and recovery of structures.

Appears in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI2001)

References in corpus (2)

• Causal Inference and Causal Explanation with Background Knowledge
• Causal Discovery from a Mixture of Experimental and Observational Data

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