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SUMMARY:Machine learning & nonparametric efficiency in causal inference - 
 Edward Kennedy (Carnegie Mellon University)
DTSTART:20260120T113000Z
DTEND:20260120T123000Z
UID:TALK241780@talks.cam.ac.uk
DESCRIPTION:This short course covers the basics of efficient nonparametric
  estimation in causal inference\, including estimating equations\, TMLE\, 
 and double machine learning. It considers nonparametric efficiency bounds 
 for causal estimands\, and efficient bias-corrected estimators based on in
 fluence functions. Importantly\, these estimators yield fast rates of conv
 ergence and normal limiting distributions\, even in complex nonparametric 
 models where nuisance functions (e.g.\, propensity scores) are estimated w
 ith modern machine learning tools. The estimators are often doubly robust.
  Background in mathematical statistics is useful but not required. The wor
 kshop covers both theory and application\, including R code for implementi
 ng various methods.
LOCATION:Seminar Room 1\, Newton Institute
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