Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding

Mirthe Maria Van Diepen, Ioan Gabriel Bucur, Tom Heskes, Tom Claassen
Proceedings of the Second Conference on Causal Learning and Reasoning, PMLR 213:707-725, 2023.

Abstract

In this work, we show how to combine two popular paradigms for causal discovery from observational data in the presence of latent confounders in order to arrive at a much more informative causal model. Building on the seminal constraint-based causal discovery algorithm, FCI, we exploit the power of direct cause-effect pair identification to uncover new relationships, which can subsequently be propagated to find even more causal links in the rest of the model. This idea has been explored before, but until now always under the assumption of no latent confounders. Using our new causal direction criterion (CDC), we can finally drop this limitation. We derive inference rules for orienting additional cause-effect relations and show how to minimize the number of tests during the CDC search. In our experimental evaluations over a range of simulated data sets, the resulting FCI-CDC algorithm increases recall by between 5%-10% compared to vanilla FCI, without loss in precision.

Cite this Paper

BibTeX
@InProceedings{pmlr-v213-diepen23a, title = {Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding}, author = {Diepen, Mirthe Maria Van and Bucur, Ioan Gabriel and Heskes, Tom and Claassen, Tom}, booktitle = {Proceedings of the Second Conference on Causal Learning and Reasoning}, pages = {707--725}, year = {2023}, editor = {van der Schaar, Mihaela and Zhang, Cheng and Janzing, Dominik}, volume = {213}, series = {Proceedings of Machine Learning Research}, month = {11--14 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v213/diepen23a/diepen23a.pdf}, url = {https://proceedings.mlr.press/v213/diepen23a.html}, abstract = {In this work, we show how to combine two popular paradigms for causal discovery from observational data in the presence of latent confounders in order to arrive at a much more informative causal model. Building on the seminal constraint-based causal discovery algorithm, FCI, we exploit the power of direct cause-effect pair identification to uncover new relationships, which can subsequently be propagated to find even more causal links in the rest of the model. This idea has been explored before, but until now always under the assumption of no latent confounders. Using our new causal direction criterion (CDC), we can finally drop this limitation. We derive inference rules for orienting additional cause-effect relations and show how to minimize the number of tests during the CDC search. In our experimental evaluations over a range of simulated data sets, the resulting FCI-CDC algorithm increases recall by between 5%-10% compared to vanilla FCI, without loss in precision.} }
Endnote
%0 Conference Paper %T Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding %A Mirthe Maria Van Diepen %A Ioan Gabriel Bucur %A Tom Heskes %A Tom Claassen %B Proceedings of the Second Conference on Causal Learning and Reasoning %C Proceedings of Machine Learning Research %D 2023 %E Mihaela van der Schaar %E Cheng Zhang %E Dominik Janzing %F pmlr-v213-diepen23a %I PMLR %P 707--725 %U https://proceedings.mlr.press/v213/diepen23a.html %V 213 %X In this work, we show how to combine two popular paradigms for causal discovery from observational data in the presence of latent confounders in order to arrive at a much more informative causal model. Building on the seminal constraint-based causal discovery algorithm, FCI, we exploit the power of direct cause-effect pair identification to uncover new relationships, which can subsequently be propagated to find even more causal links in the rest of the model. This idea has been explored before, but until now always under the assumption of no latent confounders. Using our new causal direction criterion (CDC), we can finally drop this limitation. We derive inference rules for orienting additional cause-effect relations and show how to minimize the number of tests during the CDC search. In our experimental evaluations over a range of simulated data sets, the resulting FCI-CDC algorithm increases recall by between 5%-10% compared to vanilla FCI, without loss in precision.
APA
Diepen, M.M.V., Bucur, I.G., Heskes, T. & Claassen, T.. (2023). Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding. Proceedings of the Second Conference on Causal Learning and Reasoning, in Proceedings of Machine Learning Research 213:707-725 Available from https://proceedings.mlr.press/v213/diepen23a.html.

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