While invariance of causal mechanisms has inspired recent work in both robust machine learning and causal inference, causal mech- anisms often vary over domains due to, for example, population- specific differences, the context of data collection, or intervention. To discover invariant and changing mechanisms from data, we pro- pose extending the algorithmic model for causation to mechanism changes and instantiating it via Minimum Description Length. In essence, for a continuous variable ???? in multiple contexts C, we identify variables ???? as causal if the regression functions ???? : ???? → ???? have succinct descriptions in all contexts. In empirical evaluations we show that our method, Vario, reveals mechanism changes, dis- covers causal variables by invariance, and finds causal networks, such as on real-world data that gives insight into the signaling pathways in human immune cells.
History
Preferred Citation
Sara Mameche, David Kaltenpoth and Jilles Vreeken. Discovering Invariant and Changing Mechanisms from Data. In: ACM International Conference on Knowledge Discovery and Data Mining (KDD). 2022.
Primary Research Area
Trustworthy Information Processing
Name of Conference
ACM International Conference on Knowledge Discovery and Data Mining (KDD)
Legacy Posted Date
2022-07-15
Open Access Type
Unknown
BibTeX
@inproceedings{cispa_all_3728,
title = "Discovering Invariant and Changing Mechanisms from Data",
author = "Mameche, Sara and Kaltenpoth, David and Vreeken, Jilles",
booktitle="{ACM International Conference on Knowledge Discovery and Data Mining (KDD)}",
year="2022",
}