Selecting subpopulations for causal inference in regression discontinuity designs
Forastiere L, Mattei A, Pescarini J, Barreto M, Mealli F. Selecting subpopulations for causal inference in regression discontinuity designs. The Annals Of Applied Statistics 2025, 19: 1801-1825. DOI: 10.1214/24-aoas1980.Peer-Reviewed Original ResearchBolsa Familia ProgramAverage treatment effectRegression discontinuityRD assumptionsBrazilian householdsCausal effectsSubpopulation membershipRegression discontinuity designConditional cash transfer programFinite mixture approachCausal inferenceBayesian mixture modelling approachShort-term povertyLong-term povertyCash transfer programPotential outcomes approachCutoff ruleDiscontinuity designRD designHuman capitalCausal estimandsCash transfersLocal regression methodCausal risk differenceSocial programsDesign of egocentric network-based studies to estimate causal effects under interference.
Fang J, Spiegelman D, Buchanan A, Forastiere L. Design of egocentric network-based studies to estimate causal effects under interference. Statistical Methods In Medical Research 2025, 9622802251357021. PMID: 40671608, DOI: 10.1177/09622802251357021.Peer-Reviewed Original ResearchPeer education interventionEducational interventionPublic health interventionsCausal effectsHealth interventionsHIV preventionOutcomes FrameworkSample size formulaPotential outcomes frameworkInterventionRegression modelsNetwork membersJoint hypothesis testingWhole populationSize formulaOverall effectBehavioral influencesSpillover effectsIndividualsPeerIdentification strategyUntreated individualsRandomized experimentSpilloverHIV
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