Causal Additive Models with Unobserved Causal Paths and Backdoor Paths

Thong Pham, Takashi Nicholas Maeda, Shohei Shimizu
Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, PMLR 300:1585-1593, 2026.

Abstract

Causal additive models provide a tractable yet expressive framework for causal discovery in the presence of hidden variables. When unobserved backdoor or causal paths exist between two variables, their causal relationship is often unidentifiable under existing theories. We establish sufficient conditions under which causal directions can be identified in many such cases. These conditions rely on new characterizations of regression sets to determine independence among regression residuals and conditional independencies among observed variables. Building on these results, we introduce a search algorithm that incorporates these innovations and prove its soundness and completeness. Empirical evaluations demonstrate its competitive performance against state-of-the-art methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v300-pham26a, title = { Causal Additive Models with Unobserved Causal Paths and Backdoor Paths }, author = {Pham, Thong and Maeda, Takashi Nicholas and Shimizu, Shohei}, booktitle = {Proceedings of The 29th International Conference on Artificial Intelligence and Statistics}, pages = {1585--1593}, year = {2026}, editor = {Khan, Emtiyaz and Li, Yingzhen and Solin, Arno and Ramdas, Aaditya}, volume = {300}, series = {Proceedings of Machine Learning Research}, month = {02--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v300/main/assets/pham26a/pham26a.pdf}, url = {https://proceedings.mlr.press/v300/pham26a.html}, abstract = { Causal additive models provide a tractable yet expressive framework for causal discovery in the presence of hidden variables. When unobserved backdoor or causal paths exist between two variables, their causal relationship is often unidentifiable under existing theories. We establish sufficient conditions under which causal directions can be identified in many such cases. These conditions rely on new characterizations of regression sets to determine independence among regression residuals and conditional independencies among observed variables. Building on these results, we introduce a search algorithm that incorporates these innovations and prove its soundness and completeness. Empirical evaluations demonstrate its competitive performance against state-of-the-art methods. } }
Endnote
%0 Conference Paper %T Causal Additive Models with Unobserved Causal Paths and Backdoor Paths %A Thong Pham %A Takashi Nicholas Maeda %A Shohei Shimizu %B Proceedings of The 29th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2026 %E Emtiyaz Khan %E Yingzhen Li %E Arno Solin %E Aaditya Ramdas %F pmlr-v300-pham26a %I PMLR %P 1585--1593 %U https://proceedings.mlr.press/v300/pham26a.html %V 300 %X Causal additive models provide a tractable yet expressive framework for causal discovery in the presence of hidden variables. When unobserved backdoor or causal paths exist between two variables, their causal relationship is often unidentifiable under existing theories. We establish sufficient conditions under which causal directions can be identified in many such cases. These conditions rely on new characterizations of regression sets to determine independence among regression residuals and conditional independencies among observed variables. Building on these results, we introduce a search algorithm that incorporates these innovations and prove its soundness and completeness. Empirical evaluations demonstrate its competitive performance against state-of-the-art methods.
APA
Pham, T., Maeda, T.N. & Shimizu, S.. (2026). Causal Additive Models with Unobserved Causal Paths and Backdoor Paths . Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 300:1585-1593 Available from https://proceedings.mlr.press/v300/pham26a.html.

Related Material