Causal Reasoning in Graphical Time Series Models

2012/06/20 by Eichler, Michael, Didelez, Vanessa · 2 citations
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Methodology (stat.ME)

paper · doi:10.48550/arxiv.1206.5246

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.

Cited by

• Identifiability by common backdoor in summary causal graphs of time series
• Complete Characterization for Adjustment in Summary Causal Graphs of Time Series

Related

• Causal Identification in Time Series Models
• Characterization of causal ancestral graphs for time series with latent confounders
• Using Time Structure to Estimate Causal Effects
• Characterization of causal ancestral graphs for time series with latent confounders
• Partially Intervenable Causal Models