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SUMMARY:On the estimation of causal associations with a binary outcome - S
 tephen Burgess (University of Cambridge)
DTSTART:20110428T141500Z
DTEND:20110428T151500Z
UID:TALK30172@talks.cam.ac.uk
CONTACT:Elena Yudovina
DESCRIPTION:Estimation of causal associations is fundamental in every area
  of science. From estimation of the effect of quantitative easing on the U
 K economy to the effect of alcohol intake on blood pressure\, question of 
 cause and effect are at the heart of all applied research. We shall explor
 e the method of instrumental variables\, which can be used to estimate cau
 sal associations from observational data free from bias due to confounding
  (correlation between the causal factor of interest and a competing risk f
 actor) and reverse causation (true causal effect of the `outcome' on the c
 ausal factor of interest. Of particular interest will be the case where th
 e outcome is binary\, as the non-linear association between causal factor 
 and outcome leads to differences in the estimated causal effect from what 
 would be estimated from a regression in some cases known as non-collapsibi
 lity. The talk will be illustrated throughout with data on the association
  between inflammation and coronary heart disease.
LOCATION:CMS\, MR11
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