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SUMMARY:Some issues for causal inference in observational epidemiology - N
 uala Sheehan\, Department of Health Sciences\, University of Leicester
DTSTART:20100316T110000Z
DTEND:20100316T120000Z
UID:TALK21729@talks.cam.ac.uk
CONTACT:Michael Sweeting
DESCRIPTION:Inferring causality from observational data is difficult as it
  is not always clear which of two associated variables is the cause\, whic
 h the effect\, or whether both are common effects of a third unobserved va
 riable\, or confounder.  Instrumental variable (IV) methods are widely use
 d in econometrics and provide a means to test for or estimate a causal eff
 ect when confounding is believed to be present but not fully understood.  
 Mendelian randomisation refers to the situation when the instrument is a g
 enetic variant and has received a lot of attention in the epidemiological 
 literature recently. \n\nTesting for the presence of a causal effect is ge
 nerally straightforward but point estimates are only obtainable under addi
 tional parametric and distributional assumptions.  Moreover\, there are se
 veral IV estimators to choose from\, all requiring different assumptions a
 nd targeting different causal effects. As is usual in causal inference\, s
 uch assumptions cannot always be verified from the data and have to be sup
 ported by background knowledge. Problems particularly arise when the outco
 me of interest is a binary indicator of disease status\, for example\, alt
 hough there are special cases where these can be satisfactorily addressed.
 \n\nI will introduce a formal causal framework and discuss Mendelian rando
 misation in the context of binary epidemiological outcomes. I will explore
  the different assumptions underpinning the various IV estimators and show
  how violations of these assumptions may bias the results of an analysis. 
LOCATION:Large Seminar Room\, 1st Floor\, Institute of Public Health\, Uni
 versity Forvie Site\, Robinson Way\, Cambridge
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