Causal Discovery with Hidden Confounders using the Algorithmic Markov Condition

David Kaltenpoth, Jilles Vreeken
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1016-1026, 2023.

Abstract

Causal sufficiency is a cornerstone assumption in causal discovery. It is, however, both unlikely to hold in practice as well as unverifiable. When it does not hold, existing methods struggle to return meaningful results. In this paper, we show how to discover the causal network over both observed and unobserved variables. Moreover, we show that the causal model is identifiable in the sparse linear Gaussian case. More generally, we extend the algorithmic Markov condition to include latent confounders. We propose a consistent score based on the Minimum Description Length principle to discover the full causal network, including latent confounders. Based on this score, we develop an effective algorithm that finds those sets of nodes for which the addition of a confounding factor $Z$ is most beneficial, then fits a new causal network over both observed as well as inferred latent variables.

Cite this Paper

BibTeX
@InProceedings{pmlr-v216-kaltenpoth23a, title = {Causal Discovery with Hidden Confounders using the Algorithmic {M}arkov Condition}, author = {Kaltenpoth, David and Vreeken, Jilles}, booktitle = {Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence}, pages = {1016--1026}, year = {2023}, editor = {Evans, Robin J. and Shpitser, Ilya}, volume = {216}, series = {Proceedings of Machine Learning Research}, month = {31 Jul--04 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v216/kaltenpoth23a/kaltenpoth23a.pdf}, url = {https://proceedings.mlr.press/v216/kaltenpoth23a.html}, abstract = {Causal sufficiency is a cornerstone assumption in causal discovery. It is, however, both unlikely to hold in practice as well as unverifiable. When it does not hold, existing methods struggle to return meaningful results. In this paper, we show how to discover the causal network over both observed and unobserved variables. Moreover, we show that the causal model is identifiable in the sparse linear Gaussian case. More generally, we extend the algorithmic Markov condition to include latent confounders. We propose a consistent score based on the Minimum Description Length principle to discover the full causal network, including latent confounders. Based on this score, we develop an effective algorithm that finds those sets of nodes for which the addition of a confounding factor $Z$ is most beneficial, then fits a new causal network over both observed as well as inferred latent variables.} }
Endnote
%0 Conference Paper %T Causal Discovery with Hidden Confounders using the Algorithmic Markov Condition %A David Kaltenpoth %A Jilles Vreeken %B Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2023 %E Robin J. Evans %E Ilya Shpitser %F pmlr-v216-kaltenpoth23a %I PMLR %P 1016--1026 %U https://proceedings.mlr.press/v216/kaltenpoth23a.html %V 216 %X Causal sufficiency is a cornerstone assumption in causal discovery. It is, however, both unlikely to hold in practice as well as unverifiable. When it does not hold, existing methods struggle to return meaningful results. In this paper, we show how to discover the causal network over both observed and unobserved variables. Moreover, we show that the causal model is identifiable in the sparse linear Gaussian case. More generally, we extend the algorithmic Markov condition to include latent confounders. We propose a consistent score based on the Minimum Description Length principle to discover the full causal network, including latent confounders. Based on this score, we develop an effective algorithm that finds those sets of nodes for which the addition of a confounding factor $Z$ is most beneficial, then fits a new causal network over both observed as well as inferred latent variables.
APA
Kaltenpoth, D. & Vreeken, J.. (2023). Causal Discovery with Hidden Confounders using the Algorithmic Markov Condition. Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 216:1016-1026 Available from https://proceedings.mlr.press/v216/kaltenpoth23a.html.

Related Material