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 axe 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
Text
Tian and Pearl. "Causal Discovery from Changes." Conference on Uncertainty in Artificial Intelligence, 2001.Markdown
[Tian and Pearl. "Causal Discovery from Changes." Conference on Uncertainty in Artificial Intelligence, 2001.](https://mlanthology.org/uai/2001/tian2001uai-causal/)BibTeX
@inproceedings{tian2001uai-causal,
title = {{Causal Discovery from Changes}},
author = {Tian, Jin and Pearl, Judea},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2001},
pages = {512-521},
url = {https://mlanthology.org/uai/2001/tian2001uai-causal/}
}