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CATEGORIES:Lecture
DESCRIPTION:Targeted Function Balancing -\nThis talk introduces Targeted Fu
nction Balancing (TFB)\, a covariate balancing framework for estimating the
causal effect of a binary intervention on an outcome. TFB first regresses
the outcome on observed covariates\, and then selects weights that balance
functions (of the covariates) that are probabilistically near the resulting
regression function. Notably\, TFB demonstrates that intentionally leaving
imbalance in some covariates can increase efficiency without introducing b
ias\, challenging traditions that warn against imbalance in any variable. F
urther\, TFB is entirely defined by the choice of regression estimator and
an estimator for the resulting functionâ€™s variance\, turning the problem of
how best to balance the covariates into how best to model the outcome\, an
d inviting the application of machine learning models.
DTEND:20221110T014500Z
DTSTAMP:20240518T215151Z
DTSTART:20221110T004500Z
LOCATION:Eliot 314
SEQUENCE:0
SUMMARY:Statistics Job Talk: Lenny Wainstein\, Reed College
UID:tag:localist.com\,2008:EventInstance_41546631324431
URL:https://events.reed.edu/event/statistics_job_talk_lenny_wainstein_reed_
college
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