Causal Effect Identification in LiNGAM Models with Latent Confounders

Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Mathias Drton, Negar Kiyavash
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:48468-48493, 2024.

Abstract

We study the generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables. We consider the problem in two main settings: When the causal graph is known a priori, and when it is unknown. In both settings, we provide a complete graphical characterization of the identifiable direct or total causal effects among observed variables. Moreover, we propose efficient algorithms to certify the graphical conditions. Finally, we propose an adaptation of the reconstruction independent component analysis (RICA) algorithm that estimates the causal effects from the observational data given the causal graph. Experimental results show the effectiveness of the proposed method in estimating the causal effects.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-tramontano24a, title = {Causal Effect Identification in {L}i{NGAM} Models with Latent Confounders}, author = {Tramontano, Daniele and Kivva, Yaroslav and Salehkaleybar, Saber and Drton, Mathias and Kiyavash, Negar}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {48468--48493}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/tramontano24a/tramontano24a.pdf}, url = {https://proceedings.mlr.press/v235/tramontano24a.html}, abstract = {We study the generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables. We consider the problem in two main settings: When the causal graph is known a priori, and when it is unknown. In both settings, we provide a complete graphical characterization of the identifiable direct or total causal effects among observed variables. Moreover, we propose efficient algorithms to certify the graphical conditions. Finally, we propose an adaptation of the reconstruction independent component analysis (RICA) algorithm that estimates the causal effects from the observational data given the causal graph. Experimental results show the effectiveness of the proposed method in estimating the causal effects.} }
Endnote
%0 Conference Paper %T Causal Effect Identification in LiNGAM Models with Latent Confounders %A Daniele Tramontano %A Yaroslav Kivva %A Saber Salehkaleybar %A Mathias Drton %A Negar Kiyavash %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-tramontano24a %I PMLR %P 48468--48493 %U https://proceedings.mlr.press/v235/tramontano24a.html %V 235 %X We study the generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables. We consider the problem in two main settings: When the causal graph is known a priori, and when it is unknown. In both settings, we provide a complete graphical characterization of the identifiable direct or total causal effects among observed variables. Moreover, we propose efficient algorithms to certify the graphical conditions. Finally, we propose an adaptation of the reconstruction independent component analysis (RICA) algorithm that estimates the causal effects from the observational data given the causal graph. Experimental results show the effectiveness of the proposed method in estimating the causal effects.
APA
Tramontano, D., Kivva, Y., Salehkaleybar, S., Drton, M. & Kiyavash, N.. (2024). Causal Effect Identification in LiNGAM Models with Latent Confounders. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:48468-48493 Available from https://proceedings.mlr.press/v235/tramontano24a.html.

Related Material