On Granger causality and the effect of interventions in time series

M. Eichler, V. Didelez

Research output: Contribution to journalArticleAcademicpeer-review

28 Citations (Scopus)

Abstract

We combine two approaches to causal reasoning. Granger causality, on the one hand, is popular in fields like econometrics, where randomised experiments are not very common. Instead information about the dynamic development of a system is explicitly modelled and used to define potentially causal relations. On the other hand, the notion of causality as effect of interventions is predominant in fields like medical statistics or computer science. In this paper, we consider the effect of external, possibly multiple and sequential, interventions in a system of multivariate time series, the Granger causal structure of which is taken to be known. We address the following questions: under what assumptions about the system and the interventions does Granger causality inform us about the effectiveness of interventions, and when does the possibly smaller system of observable times series allow us to estimate this effect? For the latter we derive criteria that can be checked graphically and are in the same spirit as Pearl's back-door and front-door criteria (Pearl 1995).

Original languageEnglish
Pages (from-to)3-32
Number of pages30
JournalLifetime Data Analysis
Volume16
Issue number1
DOIs
Publication statusPublished - 1 Jan 2010

Keywords

  • Causality
  • Causal inference
  • Decision theory
  • Graphical models
  • Time series
  • GRAPHICAL MODELS
  • MARKOV-PROCESSES
  • INFERENCE
  • DIAGRAMS
  • INDEPENDENCE
  • NONCAUSALITY
  • STATISTICS

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