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Journal of Healthcare Engineering
Volume 2 (2011), Issue 1, Pages 111-116
http://dx.doi.org/10.1260/2040-2295.2.1.111
Research Article

Brief Note: Introducing the Language of Causal Analysis

Michael Joffe

Department of Epidemiology and Biostatistics, Imperial College London, Norfolk Place, London W2 1PG, UK

Copyright © 2011 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. F. Alemi, M. Zargoush, J. L. Oakes, and H. Edrees, “A simple method for causal analysis of return on IT investment,” Journal of Healthcare Engineering, vol. 2, no. 1, pp. 43–53, 2011. View at Google Scholar
  2. J. Pearl, “Causal inference in the health sciences: a conceptual introduction,” Health services and outcomes research methodology, vol. 2, pp. 189–220, 2002. View at Google Scholar
  3. M. L. Petersen, S. E. Sinisi, and M. J. van der Laan, “Estimation of direct causal effects,” Epidemiology, vol. 17, no. 3, pp. 276–84, 2006. View at Google Scholar
  4. S. Goetgeluk, S. Vansteelandt, and E. Goetghebeur, “Estimation of controlled direct effects,” J R Statist Soc B, vol. 70, no. 5, pp. 1049–66, 2009. View at Google Scholar
  5. M. M. Glymour and S. Greenland, “Causal diagrams,” in Modern epidemiology, K. J. Rothman, S. Greenland, and T. L. Lash, Eds., Wolters Kluwer/Lippincott Williams & Wilkins, Philadelphia, 2008. View at Google Scholar
  6. J. M. Robins, “Data, design, and background knowledge in etiologic inference,” Epidemiology, vol. 11, no. 3, pp. 313–20, 2001. View at Google Scholar
  7. P. P. Howards, E. F. Schisterman, and P. J. Heagerty, “Potential confounding by exposure history and prior outcomes – an example from perinatal epidemiology,” Epidemiology, vol. 18, no. 5, pp. 544–51, 2007. View at Google Scholar
  8. J. W. Hogan, “Bringing causal models into the mainstream,” Epidemiology, vol. 20, no. 3, pp. 431–32, 2009. View at Google Scholar
  9. M. Joffe and J. Mindell, “Complex causal process diagrams for analyzing the health impacts of policy interventions,” Am J Public Health, vol. 96, pp. 473–79, 2006. View at Google Scholar
  10. J. Pearl, Probabilistic reasoning in intelligent systems: networks of plausible inference, Morgan Kaufman Publishers, Inc., San Francisco, 1988.
  11. S. L. Lauritzen, Graphical models, Oxford University Press, Oxford, 2006.
  12. J. Pearl, Causality: models, reasoning and inference, Cambridge University Press, New York, 2000.
  13. M. Joffe, “Causality and evidence discovery in epidemiology,” in Proceedings of ESF workshops [provisional title], M. Weber, Ed., Springer, accepted for publication.
  14. A. B. Hill, “The environment and disease: association or causation?” Proc Royal Soc Med, vol. 58, pp. 295–300, 1965. View at Google Scholar
  15. C. M. Bishop, “Latent variable models,” in Learning in Graphical Models, M. I. Jordan, Ed., pp. 371–403, MIT Press, 1999, http://research.microsoft.com/en-us/um/people/cmbishop/downloads/bishop-latent-erice-99.pdf, [accessed 31 May 2010]. View at Google Scholar