22. Causal Inference in Time Series Analysis

  1. Carlo Berzuini,
  2. Philip Dawid and
  3. Luisa Bernardinelli
  1. Michael Eichler

Published Online: 25 JUN 2012

DOI: 10.1002/9781119945710.ch22

Causality: Statistical Perspectives and Applications

Causality: Statistical Perspectives and Applications

How to Cite

Eichler, M. (2012) Causal Inference in Time Series Analysis, in Causality: Statistical Perspectives and Applications (eds C. Berzuini, P. Dawid and L. Bernardinelli), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781119945710.ch22

Editor Information

  1. Statistical Laboratory, Centre for Mathematical Sciences, University of Cambridge, Cambridge, UK

Author Information

  1. Department of Quantitative Economics, Maastricht University, Maastricht, The Netherlands

Publication History

  1. Published Online: 25 JUN 2012
  2. Published Print: 13 JUL 2012

Book Series:

  1. Wiley Series in Probability and Statistics

Book Series Editors:

  1. Walter A. Shewhart and
  2. Samuel S. Wilks

ISBN Information

Print ISBN: 9780470665565

Online ISBN: 9781119945710

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Keywords:

  • causal inference in time series, Granger causality empirical;
  • causal relationship identification, in scientific research;
  • time series, causal inference and temporal precedence;
  • Granger causality in measure of association, with spurious causalities;
  • Granger causality in the broader framework, graph-based causal inference;
  • causal relationships, for aspects of system and change;
  • DGP, a direct structural causality in dynamic structural systems;
  • cause–effect, and Granger causality, in terms of DGP;
  • Sims causality, direct and indirect causal effects, for total causality;
  • learning causal structures, FCI and score-based model selection

Summary

This chapter contains sections titled:

  • Introduction

  • Causality for time series

  • Graphical representations for time series

  • Representation of systems with latent variables

  • Identification of causal effects

  • Learning causal structures

  • A new parametric model

  • Concluding remarks

  • References