Linking Granger Causality and the Pearl Causal Model with Settable Systems

Halbert White, Karim Chalak, Xun Lu
Proceedings of the Neural Information Processing Systems Mini-Symposium on Causality in Time Series, PMLR 12:1-29, 2011.

Abstract

The causal notions embodied in the concept of Granger causality have been argued to belong to a different category than those of Judea Pearl’s Causal Model, and so far their relation has remained obscure. Here, we demonstrate that these concepts are in fact closely linked by showing how each relates to straightforward notions of direct causality embodied in settable systems, an extension and refinement of the Pearl Causal Model designed to accommodate optimization, equilibrium, and learning. We then provide straightforward practical methods to test for direct causality using tests for Granger causality.

Cite this Paper

BibTeX
@InProceedings{pmlr-v12-white11, title = {Linking Granger Causality and the Pearl Causal Model with Settable Systems}, author = {White, Halbert and Chalak, Karim and Lu, Xun}, booktitle = {Proceedings of the Neural Information Processing Systems Mini-Symposium on Causality in Time Series}, pages = {1--29}, year = {2011}, editor = {Popescu, Florin and Guyon, Isabelle}, volume = {12}, series = {Proceedings of Machine Learning Research}, address = {Vancouver, Canada}, month = {10 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v12/white11/white11.pdf}, url = {https://proceedings.mlr.press/v12/white11.html}, abstract = {The causal notions embodied in the concept of Granger causality have been argued to belong to a different category than those of Judea Pearl’s Causal Model, and so far their relation has remained obscure. Here, we demonstrate that these concepts are in fact closely linked by showing how each relates to straightforward notions of direct causality embodied in settable systems, an extension and refinement of the Pearl Causal Model designed to accommodate optimization, equilibrium, and learning. We then provide straightforward practical methods to test for direct causality using tests for Granger causality.} }
Endnote
%0 Conference Paper %T Linking Granger Causality and the Pearl Causal Model with Settable Systems %A Halbert White %A Karim Chalak %A Xun Lu %B Proceedings of the Neural Information Processing Systems Mini-Symposium on Causality in Time Series %C Proceedings of Machine Learning Research %D 2011 %E Florin Popescu %E Isabelle Guyon %F pmlr-v12-white11 %I PMLR %P 1--29 %U https://proceedings.mlr.press/v12/white11.html %V 12 %X The causal notions embodied in the concept of Granger causality have been argued to belong to a different category than those of Judea Pearl’s Causal Model, and so far their relation has remained obscure. Here, we demonstrate that these concepts are in fact closely linked by showing how each relates to straightforward notions of direct causality embodied in settable systems, an extension and refinement of the Pearl Causal Model designed to accommodate optimization, equilibrium, and learning. We then provide straightforward practical methods to test for direct causality using tests for Granger causality.
RIS
TY - CPAPER TI - Linking Granger Causality and the Pearl Causal Model with Settable Systems AU - Halbert White AU - Karim Chalak AU - Xun Lu BT - Proceedings of the Neural Information Processing Systems Mini-Symposium on Causality in Time Series DA - 2011/03/03 ED - Florin Popescu ED - Isabelle Guyon ID - pmlr-v12-white11 PB - PMLR DP - Proceedings of Machine Learning Research VL - 12 SP - 1 EP - 29 L1 - http://proceedings.mlr.press/v12/white11/white11.pdf UR - https://proceedings.mlr.press/v12/white11.html AB - The causal notions embodied in the concept of Granger causality have been argued to belong to a different category than those of Judea Pearl’s Causal Model, and so far their relation has remained obscure. Here, we demonstrate that these concepts are in fact closely linked by showing how each relates to straightforward notions of direct causality embodied in settable systems, an extension and refinement of the Pearl Causal Model designed to accommodate optimization, equilibrium, and learning. We then provide straightforward practical methods to test for direct causality using tests for Granger causality. ER -
APA
White, H., Chalak, K. & Lu, X.. (2011). Linking Granger Causality and the Pearl Causal Model with Settable Systems. Proceedings of the Neural Information Processing Systems Mini-Symposium on Causality in Time Series, in Proceedings of Machine Learning Research 12:1-29 Available from https://proceedings.mlr.press/v12/white11.html.

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