Causal Reasoning in Graphical Time Series Models
Methodology
2012-06-26 v1 Artificial Intelligence
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.
Keywords
Cite
@article{arxiv.1206.5246,
title = {Causal Reasoning in Graphical Time Series Models},
author = {Michael Eichler and Vanessa Didelez},
journal= {arXiv preprint arXiv:1206.5246},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (UAI2007)