Filter, Rank, and Prune: Learning Linear Cyclic Gaussian Graphical Models

Soheun Yi, Sanghack Lee
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:1135-1143, 2024.

Abstract

Causal structures in the real world often exhibit cycles naturally due to equilibrium, homeostasis, or feedback. However, causal discovery from observational studies regarding cyclic models has not been investigated extensively because the underlying structure of a linear cyclic structural equation model (SEM) cannot be determined solely from observational data. Inspired by the Bayesian information Criterion (BIC), we construct a score function that assesses both accuracy and sparsity of the structure to determine which linear Gaussian SEM is the best when only observational data is given. Then, we formulate a causal discovery problem as an optimization problem of the measure and propose the Filter, Rank, and Prune (FRP) method for solving it. We empirically demonstrate that our method outperforms competitive cyclic causal discovery baselines.

Cite this Paper

BibTeX
@InProceedings{pmlr-v238-yi24a, title = {Filter, Rank, and Prune: Learning Linear Cyclic {G}aussian Graphical Models}, author = {Yi, Soheun and Lee, Sanghack}, booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics}, pages = {1135--1143}, year = {2024}, editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen}, volume = {238}, series = {Proceedings of Machine Learning Research}, month = {02--04 May}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v238/yi24a/yi24a.pdf}, url = {https://proceedings.mlr.press/v238/yi24a.html}, abstract = {Causal structures in the real world often exhibit cycles naturally due to equilibrium, homeostasis, or feedback. However, causal discovery from observational studies regarding cyclic models has not been investigated extensively because the underlying structure of a linear cyclic structural equation model (SEM) cannot be determined solely from observational data. Inspired by the Bayesian information Criterion (BIC), we construct a score function that assesses both accuracy and sparsity of the structure to determine which linear Gaussian SEM is the best when only observational data is given. Then, we formulate a causal discovery problem as an optimization problem of the measure and propose the Filter, Rank, and Prune (FRP) method for solving it. We empirically demonstrate that our method outperforms competitive cyclic causal discovery baselines.} }
Endnote
%0 Conference Paper %T Filter, Rank, and Prune: Learning Linear Cyclic Gaussian Graphical Models %A Soheun Yi %A Sanghack Lee %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta %E Stephan Mandt %E Yingzhen Li %F pmlr-v238-yi24a %I PMLR %P 1135--1143 %U https://proceedings.mlr.press/v238/yi24a.html %V 238 %X Causal structures in the real world often exhibit cycles naturally due to equilibrium, homeostasis, or feedback. However, causal discovery from observational studies regarding cyclic models has not been investigated extensively because the underlying structure of a linear cyclic structural equation model (SEM) cannot be determined solely from observational data. Inspired by the Bayesian information Criterion (BIC), we construct a score function that assesses both accuracy and sparsity of the structure to determine which linear Gaussian SEM is the best when only observational data is given. Then, we formulate a causal discovery problem as an optimization problem of the measure and propose the Filter, Rank, and Prune (FRP) method for solving it. We empirically demonstrate that our method outperforms competitive cyclic causal discovery baselines.
APA
Yi, S. & Lee, S.. (2024). Filter, Rank, and Prune: Learning Linear Cyclic Gaussian Graphical Models. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:1135-1143 Available from https://proceedings.mlr.press/v238/yi24a.html.

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