96 research outputs found
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
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