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.

Appears in Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (UAI2007)

References in corpus (2)

• Probabilistic Evaluation of Sequential Plans from Causal Models with Hidden Variables
• Asymmetric separation for local independence graphs

Cited by in corpus (2)

• The Temporal Logic of Causal Structures
• Learning Why Things Change: The Difference-Based Causality Learner