Abstract
PDS 69: Methods and statistics, Johan Friso Foyer, Floor 1, August 26, 2019, 4:30 PM - 5:30 PM It can be challenging to conduct causal inference from observational or quasi-experimental studies. The distinction between observation and experiment is important, and between the extremes of a randomized experiment and a purely observational study there is a continuum. Natural experiments may be placed on this continuum, and their position can inform accurate assessment of uncertainty due to unmeasured or residual confounding. In this talk we introduce a mathematical algorithm that can be used to compute a confounding interval. This confounding interval differs from classical statistical confidence intervals in a few important ways to be discussed. This confounding interval has been designed for use within the field of environmental epidemiology. We demonstrate this causal inference methodology within the context of a study of the effects of polybrominated diphenyl ether exposure on neurodevelopment.
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