Context-Specific Causal Discovery for Categorical Data Using Staged Trees

Manuele Leonelli, Gherardo Varando
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:8871-8888, 2023.

Abstract

Causal discovery algorithms aim at untangling complex causal relationships from data. Here, we study causal discovery and inference methods based on staged tree models, which can represent complex and asymmetric causal relationships between categorical variables. We provide a first graphical representation of the equivalence class of a staged tree, by looking only at a specific subset of its underlying independences. We further define a new pre-metric, inspired by the widely used structural intervention distance, to quantify the closeness between two staged trees in terms of their corresponding causal inference statements. A simulation study highlights the efficacy of staged trees in uncovering complexes, asymmetric causal relationships from data, and real-world data applications illustrate their use in practical causal analysis.

Cite this Paper

BibTeX
@InProceedings{pmlr-v206-leonelli23a, title = {Context-Specific Causal Discovery for Categorical Data Using Staged Trees}, author = {Leonelli, Manuele and Varando, Gherardo}, booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics}, pages = {8871--8888}, year = {2023}, editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem}, volume = {206}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v206/leonelli23a/leonelli23a.pdf}, url = {https://proceedings.mlr.press/v206/leonelli23a.html}, abstract = {Causal discovery algorithms aim at untangling complex causal relationships from data. Here, we study causal discovery and inference methods based on staged tree models, which can represent complex and asymmetric causal relationships between categorical variables. We provide a first graphical representation of the equivalence class of a staged tree, by looking only at a specific subset of its underlying independences. We further define a new pre-metric, inspired by the widely used structural intervention distance, to quantify the closeness between two staged trees in terms of their corresponding causal inference statements. A simulation study highlights the efficacy of staged trees in uncovering complexes, asymmetric causal relationships from data, and real-world data applications illustrate their use in practical causal analysis.} }
Endnote
%0 Conference Paper %T Context-Specific Causal Discovery for Categorical Data Using Staged Trees %A Manuele Leonelli %A Gherardo Varando %B Proceedings of The 26th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2023 %E Francisco Ruiz %E Jennifer Dy %E Jan-Willem van de Meent %F pmlr-v206-leonelli23a %I PMLR %P 8871--8888 %U https://proceedings.mlr.press/v206/leonelli23a.html %V 206 %X Causal discovery algorithms aim at untangling complex causal relationships from data. Here, we study causal discovery and inference methods based on staged tree models, which can represent complex and asymmetric causal relationships between categorical variables. We provide a first graphical representation of the equivalence class of a staged tree, by looking only at a specific subset of its underlying independences. We further define a new pre-metric, inspired by the widely used structural intervention distance, to quantify the closeness between two staged trees in terms of their corresponding causal inference statements. A simulation study highlights the efficacy of staged trees in uncovering complexes, asymmetric causal relationships from data, and real-world data applications illustrate their use in practical causal analysis.
APA
Leonelli, M. & Varando, G.. (2023). Context-Specific Causal Discovery for Categorical Data Using Staged Trees. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 206:8871-8888 Available from https://proceedings.mlr.press/v206/leonelli23a.html.

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