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SUMMARY:Dimension reduction\, propensity score analyses and double robustn
 ess for estimation of causal effects in observational studies - Hui Guo\, 
 Statistical Laboratory\, Cambridge
DTSTART:20090126T160000Z
DTEND:20090126T173000Z
UID:TALK16607@talks.cam.ac.uk
CONTACT:Dr Clive Bowsher
DESCRIPTION:In many medical studies\, the focus is on estimating the avera
 ge causal effect (ACE) of a treatment. If the available data are gathered 
 from observational studies where randomisation is absent\, then estimating
  the ACE becomes problematic. We aim to find a scalar propensity variable 
 to represent subjects' characteristics. Given that the response\, characte
 ristics and treatment are linearly related\, identical (different) covaria
 nce matrices of the characteristics for the treated and untreated groups r
 esult in the same (different) estimated ACEs from regressing the response 
 on the treatment and characteristics\, and on the treatment and propensity
  variable. Moreover\, if we construct an estimator by combining the respon
 se regression model and the propensity score model\, there are two chances
  to obtain an unbiased estimator for the ACE\, which is the property of do
 uble robustness.\n
LOCATION:Centre for Mathematical Sciences\, MR15
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