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What can Statistics Contribute to a Causal Understanding?

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  • ODD O. AALEN
  • ARNOLDO FRIGESSI

Abstract

. We discuss the concept of causality in a broad manner. The traditional attitude in statistics has been to shy away from the causality concept. In recent years, however, a more proactive attitude to the causality concept has developed among statisticians. On the one hand, there is the school of counterfactual causality directed towards forming a better basis for clinical trials and epidemiology. On the other hand, there is the mechanistic view of causality. These developments are discussed and set into a larger context, where the often ignored role of time is emphasized. An extension of path analysis to stochastic processes is briefly presented. Causality is furthermore discussed in relation to genetic studies and to the emerging field of systems biology. Statisticians should also relate to the philosophical content of causality, especially that found in the foundations of physics.

Suggested Citation

  • Odd O. Aalen & Arnoldo Frigessi, 2007. "What can Statistics Contribute to a Causal Understanding?," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(1), pages 155-168, March.
    • Handle: RePEc:bla:scjsta:v:34:y:2007:i:1:p:155-168
    DOI: 10.1111/j.1467-9469.2006.00549.x
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    Cited by:

    1. Daniel Commenges & Anne Gégout‐Petit, 2009. "A general dynamical statistical model with causal interpretation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 719-736, June.
    2. Bachirou O. Taddé & Hélène Jacqmin‐Gadda & Jean‐François Dartigues & Daniel Commenges & Cécile Proust‐Lima, 2020. "Dynamic modeling of multivariate dimensions and their temporal relationships using latent processes: Application to Alzheimer's disease," Biometrics, The International Biometric Society, vol. 76(3), pages 886-899, September.
    3. Karl Friston, 2009. "Causal Modelling and Brain Connectivity in Functional Magnetic Resonance Imaging," PLOS Biology, Public Library of Science, vol. 7(2), pages 1-6, February.
    4. Eichler, M. & Didelez, V., 2009. "On Granger-causality and the effect of interventions in time series," Research Memorandum 003, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    5. Petrovic, Ljiljana & Dimitrijevic, Sladjana, 2011. "Invariance of statistical causality under convergence," Statistics & Probability Letters, Elsevier, vol. 81(9), pages 1445-1448, September.
    6. Petrović, Ljiljana & Dimitrijević, Sladjana, 2012. "Causality with finite horizon of the past in continuous time," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1219-1223.

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