Causal discovery in mixed additive noise models

Ruicong Yao, Tim Verdonck, Jakob Raymaekers
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:3088-3096, 2025.

Abstract

Uncovering causal relationships in datasets that include both categorical and continuous variables is a challenging problem. The overwhelming majority of existing methods restrict their application to dealing with a single type of variable. Our contribution is a structural causal model designed to handle mixed-type data through a general function class. We present a theoretical foundation that specifies the conditions under which the directed acyclic graph underlying the causal model can be identified from observed data. In addition, we propose Mixed-type data Extension for Regression and Independence Testing (MERIT), enabling the discovery of causal connections in real-world classification settings. Our empirical studies demonstrate that MERIT outperforms its state-of-the-art competitor in causal discovery on relatively low-dimensional data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v258-yao25a, title = {Causal discovery in mixed additive noise models}, author = {Yao, Ruicong and Verdonck, Tim and Raymaekers, Jakob}, booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics}, pages = {3088--3096}, 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/yao25a/yao25a.pdf}, url = {https://proceedings.mlr.press/v258/yao25a.html}, abstract = {Uncovering causal relationships in datasets that include both categorical and continuous variables is a challenging problem. The overwhelming majority of existing methods restrict their application to dealing with a single type of variable. Our contribution is a structural causal model designed to handle mixed-type data through a general function class. We present a theoretical foundation that specifies the conditions under which the directed acyclic graph underlying the causal model can be identified from observed data. In addition, we propose Mixed-type data Extension for Regression and Independence Testing (MERIT), enabling the discovery of causal connections in real-world classification settings. Our empirical studies demonstrate that MERIT outperforms its state-of-the-art competitor in causal discovery on relatively low-dimensional data.} }
Endnote
%0 Conference Paper %T Causal discovery in mixed additive noise models %A Ruicong Yao %A Tim Verdonck %A Jakob Raymaekers %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-yao25a %I PMLR %P 3088--3096 %U https://proceedings.mlr.press/v258/yao25a.html %V 258 %X Uncovering causal relationships in datasets that include both categorical and continuous variables is a challenging problem. The overwhelming majority of existing methods restrict their application to dealing with a single type of variable. Our contribution is a structural causal model designed to handle mixed-type data through a general function class. We present a theoretical foundation that specifies the conditions under which the directed acyclic graph underlying the causal model can be identified from observed data. In addition, we propose Mixed-type data Extension for Regression and Independence Testing (MERIT), enabling the discovery of causal connections in real-world classification settings. Our empirical studies demonstrate that MERIT outperforms its state-of-the-art competitor in causal discovery on relatively low-dimensional data.
APA
Yao, R., Verdonck, T. & Raymaekers, J.. (2025). Causal discovery in mixed additive noise models. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 258:3088-3096 Available from https://proceedings.mlr.press/v258/yao25a.html.

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