Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding

Daniel Malinsky, Peter Spirtes
Proceedings of 2018 ACM SIGKDD Workshop on Causal Disocvery, PMLR 92:23-47, 2018.

Abstract

We present constraint-based and (hybrid) score-based algorithms for causal structure learning that estimate dynamic graphical models from multivariate time series data. In contrast to previous work, our methods allow for both “contemporaneous” causal relations and arbitrary unmeasured (“latent”) processes influencing observed variables. The performance of our algorithms is investigated with simulation experiments and we briefly illustrate the proposed approach on some real data from international political economy.

Cite this Paper

BibTeX
@InProceedings{pmlr-v92-malinsky18a, title = {Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding}, author = {Malinsky, Daniel and Spirtes, Peter}, booktitle = {Proceedings of 2018 ACM SIGKDD Workshop on Causal Disocvery}, pages = {23--47}, year = {2018}, editor = {Le, Thuc Duy and Zhang, Kun and Kıcıman, Emre and Hyvärinen, Aapo and Liu, Lin}, volume = {92}, series = {Proceedings of Machine Learning Research}, month = {20 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v92/malinsky18a/malinsky18a.pdf}, url = {https://proceedings.mlr.press/v92/malinsky18a.html}, abstract = {We present constraint-based and (hybrid) score-based algorithms for causal structure learning that estimate dynamic graphical models from multivariate time series data. In contrast to previous work, our methods allow for both “contemporaneous” causal relations and arbitrary unmeasured (“latent”) processes influencing observed variables. The performance of our algorithms is investigated with simulation experiments and we briefly illustrate the proposed approach on some real data from international political economy.} }
Endnote
%0 Conference Paper %T Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding %A Daniel Malinsky %A Peter Spirtes %B Proceedings of 2018 ACM SIGKDD Workshop on Causal Disocvery %C Proceedings of Machine Learning Research %D 2018 %E Thuc Duy Le %E Kun Zhang %E Emre Kıcıman %E Aapo Hyvärinen %E Lin Liu %F pmlr-v92-malinsky18a %I PMLR %P 23--47 %U https://proceedings.mlr.press/v92/malinsky18a.html %V 92 %X We present constraint-based and (hybrid) score-based algorithms for causal structure learning that estimate dynamic graphical models from multivariate time series data. In contrast to previous work, our methods allow for both “contemporaneous” causal relations and arbitrary unmeasured (“latent”) processes influencing observed variables. The performance of our algorithms is investigated with simulation experiments and we briefly illustrate the proposed approach on some real data from international political economy.
APA
Malinsky, D. & Spirtes, P.. (2018). Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding. Proceedings of 2018 ACM SIGKDD Workshop on Causal Disocvery, in Proceedings of Machine Learning Research 92:23-47 Available from https://proceedings.mlr.press/v92/malinsky18a.html.

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