.png){kind=icon}
Return
Confidence intervals for causal parameters
.png){kind=icon}
PRE
AI
DOI：10.1002/sim.4780070707.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
AbstractConsider 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.’
Journal
No journal information available
Organization
No organization information available
Cited Papers
No cited papers available