Causal Discovery from Changes
This addresses causal discovery for researchers, but appears incremental as it builds on existing change detection methods.
The paper tackles the problem of discovering causal structures by detecting local, spontaneous changes in data-generating models, presenting algorithms that output graphical representations of equivalence classes and analyzing errors in simulated experiments.
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.