Cluster-Robust Variance Estimation for Dyadic Data

Dyadic data are common in the social sciences, although inference for such settings involves accounting for a complex clustering structure. Many analyses in the social sciences fail to account for the fact that multiple dyads share a member, and that errors are thus likely correlated across these dyads. We propose a nonparametric sandwich-type robust variance estimator for linear regression to account for such clustering in dyadic data. We enumerate conditions for estimator consistency. We also extend our results to repeated and weighted observations, including directed dyads and longitudinal data, and provide an implementation for generalized linear models such as logistic regression. We examine empirical performance with simulations and applications to international relations and speed dating

Design-Based Inference for Spatial Experiments with Interference

We consider design-based causal inference in settings where randomized treatments have effects that bleed out into space in complex ways that overlap and in violation of the standard "no interference" assumption for many causal inference methods. We define a spatial "average marginalized response," which characterizes how, in expectation, units of observation that are a specified distance from an intervention point are affected by treatments at that point, averaging over effects emanating from other intervention points. We establish conditions for non-parametric identification, asymptotic distributions of estimators, and recovery of structural effects. We propose methods for both sample-theoretic and permutation-based inference. We provide illustrations using randomized field experiments on forest conservation and health