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Statistical Models for Causation

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  • David A. Freedman

    (University of California, Berkeley)

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.

Suggested Citation

  • David A. Freedman, 2006. "Statistical Models for Causation," Evaluation Review, , vol. 30(6), pages 691-713, December.
    • Handle: RePEc:sae:evarev:v:30:y:2006:i:6:p:691-713
    DOI: 10.1177/0193841X06293771
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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