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Using information on realized effects to determine prospective causal effects

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  • Marshall M. Joffe

Abstract

The potential outcomes approach to causal inference postulates that each individual has a number of possibly latent outcomes, each of which would be observed under a different treatment. For any individual, some of these outcomes will be unobservable or counterfactual. Information about post‐treatment characteristics sometimes allows statements about what would have happened if an individual or group with these characteristics had received a different treatment. These are statements about the realized effects of the treatment. Determining the likely effect of an intervention before making a decision involves inference about effects in populations defined only by characteristics observed before decisions about treatment are made. Information on realized effects can tighten bounds on these prospectively defined measures of the intervention effect. We derive formulae for the bounds and their sampling variances and illustrate these points with data from a hypothetical study of the efficacy of screening mammography.

Suggested Citation

  • Marshall M. Joffe, 2001. "Using information on realized effects to determine prospective causal effects," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 759-774.
    • Handle: RePEc:bla:jorssb:v:63:y:2001:i:4:p:759-774
    DOI: 10.1111/1467-9868.00311
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    Cited by:

    1. Qi Long & Roderick J. A. Little & Xihong Lin, 2010. "Estimating causal effects in trials involving multitreatment arms subject to non‐compliance: a Bayesian framework," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(3), pages 513-531, May.

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