Fast computation of exact confidence intervals for randomized experiments with binary outcomes
Aronow P, Chang H, Lopatto P. Fast computation of exact confidence intervals for randomized experiments with binary outcomes. Journal Of Econometrics 2025, 251: 106056. DOI: 10.1016/j.jeconom.2025.106056.Peer-Reviewed Original ResearchGeneral caseCentral limit theoremLarge-sample approximationsBinary outcomesPermutation testAverage causal effectLimit theoremRandomized experimentBalanced caseMinimal assumptionsBalanced designsConfidence intervalsPermutationCausal effectsComputational gainsSample sizeTheoremFast computationEstimandsApproximationIntervalAssumptionsRelative risk ratiosConstructionRejoinder: Nonparametric identification is not enough, but randomized controlled trials are.
Aronow P, Robins J, Saarinen T, Sävje F, Sekhon J. Rejoinder: Nonparametric identification is not enough, but randomized controlled trials are. Observational Studies 2025, 11: 85-90. PMID: 40487084, PMCID: PMC12139717, DOI: 10.1353/obs.2025.a956844.Peer-Reviewed Original ResearchRoot-nPropensity score functionRoot-n rateConditional expectation functionFinite-sampleContinuous covariatesNonparametric identificationExpectation functionUntestable assumptionsRandomized experimentScoring functionPotential outcomesEstimated averageSample sizeEstimationPropensity scoreConfidence intervalsPropensityAssumptionsCovariatesFunctionUniformityRemarks
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