Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants

Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Negar Kiyavash, Mathias Drton
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:59924-59944, 2025.

Abstract

This paper investigates causal effect identification in latent variable Linear Non-Gaussian Acyclic Models (lvLiNGAM) using higher-order cumulants, addressing two prominent setups that are challenging in the presence of latent confounding: (1) a single proxy variable that may causally influence the treatment and (2) underspecified instrumental variable cases where fewer instruments exist than treatments. We prove that causal effects are identifiable with a single proxy or instrument and provide corresponding estimation methods. Experimental results demonstrate the accuracy and robustness of our approaches compared to existing methods, advancing the theoretical and practical understanding of causal inference in linear systems with latent confounders.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-tramontano25a, title = {Causal Effect Identification in lv{L}i{NGAM} from Higher-Order Cumulants}, author = {Tramontano, Daniele and Kivva, Yaroslav and Salehkaleybar, Saber and Kiyavash, Negar and Drton, Mathias}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {59924--59944}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/tramontano25a/tramontano25a.pdf}, url = {https://proceedings.mlr.press/v267/tramontano25a.html}, abstract = {This paper investigates causal effect identification in latent variable Linear Non-Gaussian Acyclic Models (lvLiNGAM) using higher-order cumulants, addressing two prominent setups that are challenging in the presence of latent confounding: (1) a single proxy variable that may causally influence the treatment and (2) underspecified instrumental variable cases where fewer instruments exist than treatments. We prove that causal effects are identifiable with a single proxy or instrument and provide corresponding estimation methods. Experimental results demonstrate the accuracy and robustness of our approaches compared to existing methods, advancing the theoretical and practical understanding of causal inference in linear systems with latent confounders.} }
Endnote
%0 Conference Paper %T Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants %A Daniele Tramontano %A Yaroslav Kivva %A Saber Salehkaleybar %A Negar Kiyavash %A Mathias Drton %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-tramontano25a %I PMLR %P 59924--59944 %U https://proceedings.mlr.press/v267/tramontano25a.html %V 267 %X This paper investigates causal effect identification in latent variable Linear Non-Gaussian Acyclic Models (lvLiNGAM) using higher-order cumulants, addressing two prominent setups that are challenging in the presence of latent confounding: (1) a single proxy variable that may causally influence the treatment and (2) underspecified instrumental variable cases where fewer instruments exist than treatments. We prove that causal effects are identifiable with a single proxy or instrument and provide corresponding estimation methods. Experimental results demonstrate the accuracy and robustness of our approaches compared to existing methods, advancing the theoretical and practical understanding of causal inference in linear systems with latent confounders.
APA
Tramontano, D., Kivva, Y., Salehkaleybar, S., Kiyavash, N. & Drton, M.. (2025). Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:59924-59944 Available from https://proceedings.mlr.press/v267/tramontano25a.html.

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