Causal Discovery on Dependent Binary Data

Alex Chen, Qing Zhou
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:2773-2781, 2025.

Abstract

The assumption of independence between observations (units) in a dataset is prevalent across various methodologies for learning causal graphical models. However, this assumption often finds itself in conflict with real-world data, posing challenges to accurate structure learning. We propose a decorrelation-based approach for causal graph learning on dependent binary data, where the local conditional distribution is defined by a latent utility model with dependent errors across units. We develop a pairwise maximum likelihood method to estimate the covariance matrix for the dependence among the units. Then, leveraging the estimated covariance matrix, we develop an EM-like iterative algorithm to generate and de-correlate samples of the latent utility variables, which serve as de-correlated data. Any standard causal discovery method can be applied on the de-correlated data to learn the underlying causal graph. We demonstrate that the proposed de-correlation approach significantly improves the accuracy in causal graph learning, through numerical experiments on both synthetic and real-world datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v258-chen25e, title = {Causal Discovery on Dependent Binary Data}, author = {Chen, Alex and Zhou, Qing}, booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics}, pages = {2773--2781}, year = {2025}, editor = {Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz}, volume = {258}, series = {Proceedings of Machine Learning Research}, month = {03--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v258/main/assets/chen25e/chen25e.pdf}, url = {https://proceedings.mlr.press/v258/chen25e.html}, abstract = {The assumption of independence between observations (units) in a dataset is prevalent across various methodologies for learning causal graphical models. However, this assumption often finds itself in conflict with real-world data, posing challenges to accurate structure learning. We propose a decorrelation-based approach for causal graph learning on dependent binary data, where the local conditional distribution is defined by a latent utility model with dependent errors across units. We develop a pairwise maximum likelihood method to estimate the covariance matrix for the dependence among the units. Then, leveraging the estimated covariance matrix, we develop an EM-like iterative algorithm to generate and de-correlate samples of the latent utility variables, which serve as de-correlated data. Any standard causal discovery method can be applied on the de-correlated data to learn the underlying causal graph. We demonstrate that the proposed de-correlation approach significantly improves the accuracy in causal graph learning, through numerical experiments on both synthetic and real-world datasets.} }
Endnote
%0 Conference Paper %T Causal Discovery on Dependent Binary Data %A Alex Chen %A Qing Zhou %B Proceedings of The 28th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2025 %E Yingzhen Li %E Stephan Mandt %E Shipra Agrawal %E Emtiyaz Khan %F pmlr-v258-chen25e %I PMLR %P 2773--2781 %U https://proceedings.mlr.press/v258/chen25e.html %V 258 %X The assumption of independence between observations (units) in a dataset is prevalent across various methodologies for learning causal graphical models. However, this assumption often finds itself in conflict with real-world data, posing challenges to accurate structure learning. We propose a decorrelation-based approach for causal graph learning on dependent binary data, where the local conditional distribution is defined by a latent utility model with dependent errors across units. We develop a pairwise maximum likelihood method to estimate the covariance matrix for the dependence among the units. Then, leveraging the estimated covariance matrix, we develop an EM-like iterative algorithm to generate and de-correlate samples of the latent utility variables, which serve as de-correlated data. Any standard causal discovery method can be applied on the de-correlated data to learn the underlying causal graph. We demonstrate that the proposed de-correlation approach significantly improves the accuracy in causal graph learning, through numerical experiments on both synthetic and real-world datasets.
APA
Chen, A. & Zhou, Q.. (2025). Causal Discovery on Dependent Binary Data. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 258:2773-2781 Available from https://proceedings.mlr.press/v258/chen25e.html.

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