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Open Access Highly Accessed Debate

Causal inference based on counterfactuals

M Höfler

Author Affiliations

Clinical Psychology and Epidemiology, Max Planck Institute of Psychiatry, Munich, Germany

BMC Medical Research Methodology 2005, 5:28  doi:10.1186/1471-2288-5-28

Published: 13 September 2005



The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies.


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.


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.