Causal inference from experiment and observation.

Zwahlen, Marcel; Salanti, Georgia (2018). Causal inference from experiment and observation. Evidence-Based Mental Health, 21(1), pp. 34-8. BMJ Publishing Group 10.1136/eb-2017-102859

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Results from well-conducted randomised controlled studies should ideally inform on the comparative merits of treatment choices for a health condition. In the absence of this, one attempts to use evidence from the impact of treatment when administered according to decisions of the physicians and the patients (observational evidence). Naïve comparisons between treatment options using observational evidence will lead to biased results. Under certain conditions, however, it is possible to obtain valid estimates of the comparative merits of different treatments from observational data. Causal inference can be conceptualised as a framework aiming to provide valid information about causal effects of treatments using observational evidence. It can be viewed as a missing data problem in which each patient has two outcomes: the observed outcome under the treatment actually received and a counterfactual (unobserved) outcome had the patient received a different treatment. Methodological developments over the last decades clarified the appropriate conditions and methods to obtain valid comparisons. This article provides an introduction to some of these methods.

Item Type:

Journal Article (Review Article)


04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM)

UniBE Contributor:

Zwahlen, Marcel and Salanti, Georgia


600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services




BMJ Publishing Group




Tanya Karrer

Date Deposited:

09 Jan 2018 16:57

Last Modified:

24 Oct 2019 04:01

Publisher DOI:


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