Statistical Models for Causation

David A. Freedman
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David A. Freedman: University of California, Berkeley

Evaluation Review, 2006, vol. 30, issue 6, 691-713

Abstract: Experiments offer more reliable evidence on causation than observational studies, which is not to gainsay the contribution to knowledge from observation. Experiments should be analyzed as experiments, not as observational studies. A simple comparison of rates might be just the right tool, with little value added by “sophisticated†models. This article discusses current models for causation, as applied to experimental and observational data. The intention-totreat principle and the effect of treatment on the treated will also be discussed. Flaws in perprotocol and treatment-received estimates will be demonstrated.

Keywords: causation; models; experiments; observational studies; intention-to-treat; per-protocol; treatment-received; instrumental variables (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:sae:evarev:v:30:y:2006:i:6:p:691-713

DOI: 10.1177/0193841X06293771

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