Causal Discovery from Changes: a Bayesian Approach

We propose a new method of discovering causal structures, based on the detection of local, spontaneous changes in the underlying data-generating model. We derive expressions for the Bayesian score that a causal structure should obtain from streams of data produced by locally changing distributions. Simulation experiments indicate that dynamic information may improve the power of discovery up to the theoretical limits set by statistical indistinguishability.