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Causal Reasoning from Longitudinal Data

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  • Elja Arjas
  • Jan Parner

Abstract

. This paper reviews some of the key statistical ideas that are encountered when trying to find empirical support to causal interpretations and conclusions, by applying statistical methods on experimental or observational longitudinal data. In such data, typically a collection of individuals are followed over time, then each one has registered a sequence of covariate measurements along with values of control variables that in the analysis are to be interpreted as causes, and finally the individual outcomes or responses are reported. Particular attention is given to the potentially important problem of confounding. We provide conditions under which, at least in principle, unconfounded estimation of the causal effects can be accomplished. Our approach for dealing with causal problems is entirely probabilistic, and we apply Bayesian ideas and techniques to deal with the corresponding statistical inference. In particular, we use the general framework of marked point processes for setting up the probability models, and consider posterior predictive distributions as providing the natural summary measures for assessing the causal effects. We also draw connections to relevant recent work in this area, notably to Judea Pearl's formulations based on graphical models and his calculus of so‐called do‐probabilities. Two examples illustrating different aspects of causal reasoning are discussed in detail.

Suggested Citation

  • Elja Arjas & Jan Parner, 2004. "Causal Reasoning from Longitudinal Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(2), pages 171-187, June.
  • Handle: RePEc:bla:scjsta:v:31:y:2004:i:2:p:171-187
    DOI: 10.1111/j.1467-9469.2004.02-134.x
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    Cited by:

    1. Arjas Elja & Saarela Olli, 2010. "Optimal Dynamic Regimes: Presenting a Case for Predictive Inference," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-21, March.
    2. Daniel Commenges, 2019. "Dealing with death when studying disease or physiological marker: the stochastic system approach to causality," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 381-405, July.
    3. Marco Doretti & Sara Geneletti & Elena Stanghellini, 2016. "Tackling non-ignorable dropout in the presence of time varying confounding," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(5), pages 775-795, November.
    4. Mélanie Prague & Daniel Commenges & Jon Michael Gran & Bruno Ledergerber & Jim Young & Hansjakob Furrer & Rodolphe Thiébaut, 2017. "Dynamic models for estimating the effect of HAART on CD4 in observational studies: Application to the Aquitaine Cohort and the Swiss HIV Cohort Study," Biometrics, The International Biometric Society, vol. 73(1), pages 294-304, March.
    5. Vanessa Didelez, 2008. "Graphical models for marked point processes based on local independence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 245-264, February.
    6. Hendrik J. van de Brake & Frank Walter & Floor A. Rink & Peter J. M. D. Essens & Gerben S. van der Vegt, 2020. "Benefits and Disadvantages of Individuals’ Multiple Team Membership: The Moderating Role of Organizational Tenure," Journal of Management Studies, Wiley Blackwell, vol. 57(8), pages 1502-1530, December.
    7. Pearl Judea, 2010. "An Introduction to Causal Inference," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-62, February.

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