Causal Discovery from Changes
Artificial Intelligence
2013-01-14 v1
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.
Cite
@article{arxiv.1301.2312,
title = {Causal Discovery from Changes},
author = {Jin Tian and Judea Pearl},
journal= {arXiv preprint arXiv:1301.2312},
year = {2013}
}
Comments
Appears in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI2001)