B0535
Title: Approximate balancing weights for clustered observational studies
Authors: Luke Keele - University of Pennsylvania (United States) [presenting]
Abstract: Many interventions are in settings where treatments are applied to groups. For example, a reading intervention may be implemented for all students in some schools and withheld from students in other schools. When such treatments are non-randomly allocated, outcomes across the treated and control groups may differ due to the treatment or due to baseline differences between groups. When this is the case, researchers can use statistical adjustment to make treated and control groups similar in terms of observed characteristics. Recent work in statistics has developed matching methods designed for contexts where treatments are clustered. In this article, we develop an alternative to multilevel matching based on approximate balancing weights. Moreover, these methods can automatically balance interactions between group and unit-level covariates. Using simulations, we show that weighting estimators based on these methods outperform matching in terms of both balance and root-mean-squared error. We conclude with two empirical applications.
