Dear workshop community, 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 <https://arxiv.org/pdf/1901.01230.pdf>). *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> 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! * **SPECIAL ANNOUNCEMENT* * 1. See this link <https://projects.iq.harvard.edu/applied.stats.workshop-gov3009> for the full spring schedule. Best, Connor Jerzak Applied Statistics Workshop -- Graduate Student Coordinator An anonymous feedback form for the workshop can be found here at this link <https://docs.google.com/forms/d/e/1FAIpQLScp4lPVBtp4Akf6K6ggmfcTUSIUHEJX89-CU8HWrQPpe9pjTw/viewform?usp=sf_link>. Workshop listserv sign-up at this link <https://lists.fas.harvard.edu/mailman/listinfo/gov3009-l>.