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Discovering Invariant and Changing Mechanisms from Data

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conference contribution
posted on 2023-11-29, 18:21 authored by Sara Mameche, David Kaltenpoth, Jilles VreekenJilles Vreeken
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.

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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", }

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