Causal inference based on counterfactuals.
BACKGROUND
The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies.


CONCLUSIONS
This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures.


CONCLUSIONS
Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept.
Höfler
M
M
Clinical Psychology and Epidemiology, Max Planck Institute of Psychiatry, Munich, Germany. hoefler@mpipsykl.mpg.de
D015984:Causality; D015986:Confounding Factors, Epidemiologic; D003657:Decision Making; D015987:Effect Modifier, Epidemiologic; D015340:Epidemiologic Research Design; D006801:Humans; D015233:Models, Statistical; D015588:Observer Variation