A0236
Title: Instrumental variables estimation in cluster randomized trials with noncompliance
Authors: Luke Keele - University of Pennsylvania (United States) [presenting]
Abstract: Many policy evaluations occur in settings with treatment randomized at the cluster level and there is treatment noncompliance at the unit level within each cluster. For example, villages might be assigned to treatment and control, but residents in each village may choose to not comply with their assigned treatment status. When noncompliance is present, investigators may choose to focus attention on either intention to treat effects or the treatment effect among the units that comply. When analysts focus on the effect among compliers, the instrumental variables framework can be used to evaluate identify and estimate causal effects. While a large literature exists on instrumental variables estimation methods, relatively little work has been focused on settings with clustered treatments. We review extant methods for instrumental variable estimation in clustered designs. We then show that these methods depend on assumptions that are often unrealistic in applied settings. In response, we develop an estimation method that relaxes these assumptions. Specifically, our method allows for possible treatment effect heterogeneity that is correlated with cluster size and uses a finite sample variance estimator.
