The paper proposes unbiased dyadic-data estimators of the global average treatment effect under dyadic interference, with convergence rates, a central limit theorem, and conservative variance estimators.
The variance decomposition terms in (S.3) can be categorised by subscript overlap patterns: (A)
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Causal inference with dyadic data in randomized experiments
The paper proposes unbiased dyadic-data estimators of the global average treatment effect under dyadic interference, with convergence rates, a central limit theorem, and conservative variance estimators.