Develops an empirical success ranking rule for treatment saturations under clustered network interference and derives non-asymptotic regret bounds depending on a single combinatorial network summary.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Develops covariate-adjusted estimators for treatment effects under interference that achieve asymptotic unbiasedness and a no-harm variance guarantee relative to the unadjusted estimator.
citing papers explorer
-
Ranking Treatment Saturations under Clustered Network Interference
Develops an empirical success ranking rule for treatment saturations under clustered network interference and derives non-asymptotic regret bounds depending on a single combinatorial network summary.
-
Covariate Adjustment Cannot Hurt: Treatment Effect Estimation under Interference with Low-Order Outcome Interactions
Develops covariate-adjusted estimators for treatment effects under interference that achieve asymptotic unbiasedness and a no-harm variance guarantee relative to the unadjusted estimator.