Causal Reasoning in Graphical Time Series Models
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.
Cite
Text
Eichler and Didelez. "Causal Reasoning in Graphical Time Series Models." Conference on Uncertainty in Artificial Intelligence, 2007. doi:10.5555/3020488.3020502Markdown
[Eichler and Didelez. "Causal Reasoning in Graphical Time Series Models." Conference on Uncertainty in Artificial Intelligence, 2007.](https://mlanthology.org/uai/2007/eichler2007uai-causal/) doi:10.5555/3020488.3020502BibTeX
@inproceedings{eichler2007uai-causal,
title = {{Causal Reasoning in Graphical Time Series Models}},
author = {Eichler, Michael and Didelez, Vanessa},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2007},
pages = {109-116},
doi = {10.5555/3020488.3020502},
url = {https://mlanthology.org/uai/2007/eichler2007uai-causal/}
}