A0274
Title: Approximate balancing weights for clustered observational study designs
Authors: Luke Keele - University of Pennsylvania (United States) [presenting]
Abstract: In a clustered observational study, treatment is assigned to groups, and all units within the group are exposed to the treatment. A new method is developed for statistical adjustment in clustered observational studies using approximate balancing weights, a generalization of inverse propensity score weights that solve a convex optimization problem to find a set of weights that directly minimize a measure of covariate imbalance, subject to an additional penalty on the variance of the weights. The approximate balancing weights optimization problem is tailored to both adjustment sets by deriving an upper bound on the mean square error for each case and finding weights that minimize this upper bound, linking the level of covariate balance to a bound on the bias. The procedure is implemented by specializing the bound to a random cluster-level effects model. This leads to a variance penalty that incorporates the signal signal-to-noise ratio and penalizes the weight on individuals and the total weight on groups differently according to the intra-class correlation.
