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Estimating causal effects from epidemiological data
  1. Miguel A Hernán1,
  2. James M Robins2
  1. 1Department of Epidemiology, Harvard School of Public Health, Boston, USA
  2. 2Departments of Epidemiology and Biostatistics, Harvard School of Public Health
  1. Correspondence to:
 Dr M A Hernán
 Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA; miguel_hernan{at}


In ideal randomised experiments, association is causation: association measures can be interpreted as effect measures because randomisation ensures that the exposed and the unexposed are exchangeable. On the other hand, in observational studies, association is not generally causation: association measures cannot be interpreted as effect measures because the exposed and the unexposed are not generally exchangeable. However, observational research is often the only alternative for causal inference. This article reviews a condition that permits the estimation of causal effects from observational data, and two methods—standardisation and inverse probability weighting—to estimate population causal effects under that condition. For simplicity, the main description is restricted to dichotomous variables and assumes that no random error attributable to sampling variability exists. The appendix provides a generalisation of inverse probability weighting.

  • causal inference
  • confounding
  • inverse probability weighting
  • randomisation
  • standardisation

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  • Funding: none.

  • Conflicts of interest: none declared.

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