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SUMMARY:Model selection for estimation of causal parameters - Dominik Roth
 enhaeusler (Stanford University)
DTSTART:20201030T160000Z
DTEND:20201030T170000Z
UID:TALK152455@talks.cam.ac.uk
CONTACT:Dr Sergio Bacallado
DESCRIPTION:In causal inference\, the goal is often to estimate average tr
 eatment effects. Selecting a model by cross-validation in this context can
  be problematic\, as models that exhibit great predictive accuracy can be 
 suboptimal for estimating the parameter of interest. We discuss several ap
 proaches to perform model selection in this context and compare their perf
 ormance on simulated data sets.
LOCATION: https://maths-cam-ac-uk.zoom.us/j/92821218455?pwd=aHFOZWw5bzVReU
 NYR2d5OWc1Tk15Zz09
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