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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
