We will convene for the Applied Statistics Workshop (
Gov 3009)
tomorrow on Wednesday (01/30).
The speaker is
Drew Dimmery (Facebook) who will be presenting his work "Permutation Weighting" (
paper link).
Where: CGIS Knafel Building, Room K354 (see
this link for directions).
When: Wednesday, January 30th at 12 noon - 1:30 pm.
Abstract: This
work introduces permutation weighting: a weighting estimator for
observational causal inference under general treatment regimes which
preserves arbitrary measures of covariate balance. We show that
estimating weights which obey balance constraints is equivalent to a
simple two-class classification problem between the observed data and a
permuted dataset (no matter the cardinality of treatment). Arbitrary
probabilistic classifiers may be used in this method; the hypothesis
space of the classifier corresponds to the nature of the balance
constraints imposed through the resulting weights. We show equivalence
between existing covariate balancing weight estimators and permutation
weighting and demonstrate estimation with improved efficiency through
this regime. We provide theoretical results on the consistency of
estimation of causal effects, and the necessity of balance in finite
samples. Empirical evaluations indicate that the proposed method
outperforms existing state of the art weighting methods for causal
effect estimation, even when the data generating process corresponds to
the assumptions imposed by prior work.
All are welcome! Lunch is provided!
Best,
Connor Jerzak
Applied Statistics Workshop -- Graduate Student Coordinator