A Hájek estimator for the total treatment effect in bipartite experiments is shown consistent and asymptotically normal under sparse graph assumptions, with a conservative variance estimator and covariate adjustment.
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Design-based causal inference in bipartite experiments
A Hájek estimator for the total treatment effect in bipartite experiments is shown consistent and asymptotically normal under sparse graph assumptions, with a conservative variance estimator and covariate adjustment.