Confidence intervals for causal parameters


  • James M. Robins

    1. Occupational Health Program and Department of Biostatistics, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115, U.S.A.
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Consider an unbiased follow-up study designed to investigate the causal effect of a dichotomous exposure on a dichotomous disease outcome. Under a deterministic outcome model, a standard ‘95 per cent binomial confidence interval’ may fail to cover the causal parameter of interest at the nominal rate when we take the causal parameter to be a parameter associated with the observed study population (regardless of whether the observed study population was sampled from a larger superpopulation). I propose new interval estimators that, in this setting, improve upon the performance of the standard ‘binomial confidence interval.’