Causal Structure Learning for Latent Intervened Non-stationary Data

Chenxi Liu, Kun Kuang
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:21756-21777, 2023.

Abstract

Causal structure learning can reveal the causal mechanism behind natural systems. It is well studied that the multiple domain data consisting of observational and interventional samples benefit causal identifiability. However, for non-stationary time series data, domain indexes are often unavailable, making it difficult to distinguish observational samples from interventional samples. To address these issues, we propose a novel Latent Intervened Non-stationary learning (LIN) method to make the domain indexes recovery process and the causal structure learning process mutually promote each other. We characterize and justify a possible faithfulness condition to guarantee the identifiability of the proposed LIN method. Extensive experiments on both synthetic and real-world datasets demonstrate that our method outperforms the baselines on causal structure learning for latent intervened non-stationary data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-liu23t, title = {Causal Structure Learning for Latent Intervened Non-stationary Data}, author = {Liu, Chenxi and Kuang, Kun}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {21756--21777}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/liu23t/liu23t.pdf}, url = {https://proceedings.mlr.press/v202/liu23t.html}, abstract = {Causal structure learning can reveal the causal mechanism behind natural systems. It is well studied that the multiple domain data consisting of observational and interventional samples benefit causal identifiability. However, for non-stationary time series data, domain indexes are often unavailable, making it difficult to distinguish observational samples from interventional samples. To address these issues, we propose a novel Latent Intervened Non-stationary learning (LIN) method to make the domain indexes recovery process and the causal structure learning process mutually promote each other. We characterize and justify a possible faithfulness condition to guarantee the identifiability of the proposed LIN method. Extensive experiments on both synthetic and real-world datasets demonstrate that our method outperforms the baselines on causal structure learning for latent intervened non-stationary data.} }
Endnote
%0 Conference Paper %T Causal Structure Learning for Latent Intervened Non-stationary Data %A Chenxi Liu %A Kun Kuang %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-liu23t %I PMLR %P 21756--21777 %U https://proceedings.mlr.press/v202/liu23t.html %V 202 %X Causal structure learning can reveal the causal mechanism behind natural systems. It is well studied that the multiple domain data consisting of observational and interventional samples benefit causal identifiability. However, for non-stationary time series data, domain indexes are often unavailable, making it difficult to distinguish observational samples from interventional samples. To address these issues, we propose a novel Latent Intervened Non-stationary learning (LIN) method to make the domain indexes recovery process and the causal structure learning process mutually promote each other. We characterize and justify a possible faithfulness condition to guarantee the identifiability of the proposed LIN method. Extensive experiments on both synthetic and real-world datasets demonstrate that our method outperforms the baselines on causal structure learning for latent intervened non-stationary data.
APA
Liu, C. & Kuang, K.. (2023). Causal Structure Learning for Latent Intervened Non-stationary Data. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:21756-21777 Available from https://proceedings.mlr.press/v202/liu23t.html.

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