An AutoML-Powered Architecture for Causal Discovery

Konstantina Biza, Sofia Triantafillou, Ioannis Tsamardinos
Proceedings of the 13th International Conference on Probabilistic Graphical Models, PMLR 343:36-56, 2026.

Abstract

Causal discovery is fundamental across a wide range of scientific domains. While numerous algorithms have been proposed in the literature, they often rely on methods that suffer from limitations such as strong parametric assumptions or restrictions on variable types, making them ill-suited for application to complex, real-world datasets. Recent advances in Automated Machine Learning (AutoML) leverage and tune flexible model architectures to process complex non-linear relationships and mixed data types, while automating data preprocessing for real-world applications. In this work, we design an end-to-end AutoML-based architecture for Causal Discovery (MLCD) that fully leverages these capabilities. The proposed framework consists of three main components: (i) selecting the optimal configuration for each node—including data transformations, feature sets, and predictive models and hyperparameters; (ii) constructing the graph skeleton by taking the union of the selected feature sets; and (iii) applying conditional independence tests that employ the selected optimal configurations to compute the p-values. To account for the presence of latent common causes in real-world scenarios, we formulate MLCD for causally insufficient systems, though it can easily adapt to causally sufficient environments as well. We evaluate our method on both synthetic and real-world datasets. Notably, MLCD processes raw data directly, bypassing the preprocessing pipelines required by most causal discovery methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v343-biza26a, title = {An AutoML-Powered Architecture for Causal Discovery}, author = {Biza, Konstantina and Triantafillou, Sofia and Tsamardinos, Ioannis}, booktitle = {Proceedings of the 13th International Conference on Probabilistic Graphical Models}, pages = {36--56}, year = {2026}, editor = {Camps-Valls, Gustau and Leonelli, Manuele and Varando, Gherardo}, volume = {343}, series = {Proceedings of Machine Learning Research}, month = {09--11 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v343/main/assets/biza26a/biza26a.pdf}, url = {https://proceedings.mlr.press/v343/biza26a.html}, abstract = {Causal discovery is fundamental across a wide range of scientific domains. While numerous algorithms have been proposed in the literature, they often rely on methods that suffer from limitations such as strong parametric assumptions or restrictions on variable types, making them ill-suited for application to complex, real-world datasets. Recent advances in Automated Machine Learning (AutoML) leverage and tune flexible model architectures to process complex non-linear relationships and mixed data types, while automating data preprocessing for real-world applications. In this work, we design an end-to-end AutoML-based architecture for Causal Discovery (MLCD) that fully leverages these capabilities. The proposed framework consists of three main components: (i) selecting the optimal configuration for each node—including data transformations, feature sets, and predictive models and hyperparameters; (ii) constructing the graph skeleton by taking the union of the selected feature sets; and (iii) applying conditional independence tests that employ the selected optimal configurations to compute the p-values. To account for the presence of latent common causes in real-world scenarios, we formulate MLCD for causally insufficient systems, though it can easily adapt to causally sufficient environments as well. We evaluate our method on both synthetic and real-world datasets. Notably, MLCD processes raw data directly, bypassing the preprocessing pipelines required by most causal discovery methods.} }
Endnote
%0 Conference Paper %T An AutoML-Powered Architecture for Causal Discovery %A Konstantina Biza %A Sofia Triantafillou %A Ioannis Tsamardinos %B Proceedings of the 13th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2026 %E Gustau Camps-Valls %E Manuele Leonelli %E Gherardo Varando %F pmlr-v343-biza26a %I PMLR %P 36--56 %U https://proceedings.mlr.press/v343/biza26a.html %V 343 %X Causal discovery is fundamental across a wide range of scientific domains. While numerous algorithms have been proposed in the literature, they often rely on methods that suffer from limitations such as strong parametric assumptions or restrictions on variable types, making them ill-suited for application to complex, real-world datasets. Recent advances in Automated Machine Learning (AutoML) leverage and tune flexible model architectures to process complex non-linear relationships and mixed data types, while automating data preprocessing for real-world applications. In this work, we design an end-to-end AutoML-based architecture for Causal Discovery (MLCD) that fully leverages these capabilities. The proposed framework consists of three main components: (i) selecting the optimal configuration for each node—including data transformations, feature sets, and predictive models and hyperparameters; (ii) constructing the graph skeleton by taking the union of the selected feature sets; and (iii) applying conditional independence tests that employ the selected optimal configurations to compute the p-values. To account for the presence of latent common causes in real-world scenarios, we formulate MLCD for causally insufficient systems, though it can easily adapt to causally sufficient environments as well. We evaluate our method on both synthetic and real-world datasets. Notably, MLCD processes raw data directly, bypassing the preprocessing pipelines required by most causal discovery methods.
APA
Biza, K., Triantafillou, S. & Tsamardinos, I.. (2026). An AutoML-Powered Architecture for Causal Discovery. Proceedings of the 13th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 343:36-56 Available from https://proceedings.mlr.press/v343/biza26a.html.

Related Material