Article

Causal reasoning in graphical time series Models

Authors:

Michael Eichler,

Vanessa DidelezAuthors Info & Claims

UAI'07: Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence

Pages 109 - 116

Published: 19 July 2007 Publication History

Abstract

We propose a definition of causality for time series in terms of the effect of an intervention in one component of a multivariate time series on another component at some later point in time. Conditions for identifiability, comparable to the back-door and front-door criteria, are presented and can also be verified graphically. Computation of the causal effect is derived and illustrated for the linear case.

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Cited By

Assaad CDevijver EGaussier EGössler GMeynaoui AKiyavash NMooij J(2024)Identifiability of total effects from abstractions of time series causal graphsProceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence10.5555/3702676.3702683(173-185)Online publication date: 15-Jul-2024
https://dl.acm.org/doi/10.5555/3702676.3702683

Causal reasoning in graphical time series Models
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory

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Information

Published In

UAI'07: Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence

July 2007

483 pages

ISBN:0974903930

Editors:
Ron Parr
Department of Computer Science, Stanford University, Stanford, CA
,
Linda van der Gaag
Department of Computer Science, Utrecht University, Utrecht, The Netherlands

Publisher

AUAI Press

Arlington, Virginia, United States

Publication History

Published: 19 July 2007

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Cited By

Assaad CDevijver EGaussier EGössler GMeynaoui AKiyavash NMooij J(2024)Identifiability of total effects from abstractions of time series causal graphsProceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence10.5555/3702676.3702683(173-185)Online publication date: 15-Jul-2024
https://dl.acm.org/doi/10.5555/3702676.3702683

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