Laura Forastiere, PhD
Associate Professor of BiostatisticsCards
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Affiliated Faculty, Yale Institute for Global Health
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Titles
Associate Professor of Biostatistics
Affiliated Faculty, Yale Institute for Global Health
Biography
Laura Forastiere is an Associate Professor in the Department of Biostatistics at Yale School of Public Health. Her methodological research is focused on methods for assessing causal inference for evidence-based research, exploring the mechanisms underlying the effect of an intervention including causal pathways through intermediate variables or mechanisms of peer influence and spillover between connected units. Her research explores modeling, inferential, and other methodological issues that often arise in applied problems with complex clustered and network data, and standard statistical theory and methods are no longer adequate to support the goals of the analysis. Laura is eager to apply advanced statistical methodology to provide evidence on effective strategies to improve the health and wellbeing of vulnerable populations. She is particularly interested in exploring behavioral interventions that, relying on theories of behavioral economics and social phycology, exploit social interactions and peer influence among individuals. She is involved in many program evaluations and research studies in low- and middle-income countries on malaria, HIV and other STDs, maternal and child health, nutrition, cognitive development, health insurance and microcredit. Dr. Forastiere received her Ph.D. in statistics from the University of Florence (Italy) and postdoc training in statistics and biostatistics at Harvard University. Prior to joining the Department of Biostatistics at Yale School of Public Health, she was a Postdoctoral Associate in the Yale Institute for Network Science.
Appointments
Biostatistics
Associate Professor on TermPrimary
Other Departments & Organizations
Education & Training
- PhD
- University of Florence (2015)
Research
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Overview
Medical Research Interests
Public Health Interests
ORCID
0000-0003-3721-9826
Research at a Glance
Yale Co-Authors
Publications Timeline
Research Interests
Donna Spiegelman, ScD
Sten H. Vermund, MD, PhD
Nicholas Christakis, MD, MPH, PhD
Raul U. Hernandez-Ramirez, PhD
Andrew DeWan, PhD, MPH
Ashley Hagaman, PhD, MPH
Global Health
Social Networking
Causality
Publications
2025
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 ResearchCitationsConceptsBolsa 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 ResearchAltmetricConceptsPeer education interventionEducational interventionPublic health interventionsCausal effectsHealth interventionsHIV preventionOutcomes FrameworkSample size formulaPotential outcomes frameworkInterventionRegression modelsNetwork membersJoint hypothesis testingWhole populationSize formulaOverall effectBehavioral influencesSpillover effectsIndividualsPeerIdentification strategyUntreated individualsRandomized experimentSpilloverHIVCognitive representations of social networks in isolated villages
Feltham E, Forastiere L, Christakis N. Cognitive representations of social networks in isolated villages. Nature Human Behaviour 2025, 9: 1737-1753. PMID: 40523958, PMCID: PMC12323711, DOI: 10.1038/s41562-025-02221-6.Peer-Reviewed Original ResearchCitationsAltmetricEstimating the Effects of Hypothetical Ambient PM2.5 Interventions on the Risk of Dementia Using the Parametric g-Formula in the UK Biobank Cohort
Lin C, Liu R, Sutton C, DeWan A, Forastiere L, Chen K. Estimating the Effects of Hypothetical Ambient PM2.5 Interventions on the Risk of Dementia Using the Parametric g-Formula in the UK Biobank Cohort. Environmental Health Perspectives 2025, 133: 047007. PMID: 40062909, PMCID: PMC12010936, DOI: 10.1289/ehp14723.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsRisk of dementiaParametric g-formulaUK Biobank cohortG-formulaBiobank cohortLate lifeFree of dementiaUK Biobank participantsModifiable risk factorsParticulate matterAerodynamic diameter <Annual average standardBiobank participantsHypothetical interventionAmbient particulate matterNo interventionDementiaRisk differenceInterventionRisk factorsHealth benefitsPotential health benefitsCohortRiskParticipantsHETEROGENEOUS TREATMENT AND SPILLOVER EFFECTS UNDER CLUSTERED NETWORK INTERFERENCE.
Bargagli-Stoffi F, Tortú C, Forastiere L. HETEROGENEOUS TREATMENT AND SPILLOVER EFFECTS UNDER CLUSTERED NETWORK INTERFERENCE. The Annals Of Applied Statistics 2025, 19: 28-55. PMID: 40642103, PMCID: PMC12245184, DOI: 10.1214/24-aoas1913.Peer-Reviewed Original ResearchCitations
2024
Bipartite interference and air pollution transport: estimating health effects of power plant interventions
Zigler C, Liu V, Mealli F, Forastiere L. Bipartite interference and air pollution transport: estimating health effects of power plant interventions. Biostatistics 2024, 26: kxae051. PMID: 39865699, PMCID: PMC11823286, DOI: 10.1093/biostatistics/kxae051.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsAir qualityAir quality interventionsCoal-burning power plantsImpact air qualityAir pollution transportMovement of air parcelsFlue-gas desulfurizationComplex atmospheric processesPollution sourcesPollutant transportAir parcelsPollutant emissionsAtmospheric processesHealth effectsAtmospheric modelsPollutionPlant interventionQuality interventionsMedicare hospitalizationsIntervention unitsMedicare beneficiariesOutcomes UnitZip codesPower plantsAirEstimation and inference for causal spillover effects in egocentric-network randomized trials in the presence of network membership misclassification
Chao A, Spiegelman D, Buchanan A, Forastiere L. Estimation and inference for causal spillover effects in egocentric-network randomized trials in the presence of network membership misclassification. Biostatistics 2024, 26: kxaf009. PMID: 40159413, PMCID: PMC11955068, DOI: 10.1093/biostatistics/kxaf009.Peer-Reviewed Original ResearchMeSH Keywords and ConceptsConceptsHIV Prevention Trials NetworkBehavioral changesImpact of interventionsPopulation behavior changePeer-based strategiesRandomized trialsIntervention effectsBehavioral interventionsStudy designSpillover effectsInterventionTrials NetworkInvestigate finite sample propertiesAverage spillover effectFinite sample propertiesBehavioral trainingParticipantsLeverage peer influenceMisclassificationDisseminate informationInterference settingOutcomesPeer influenceSurrogate networksSample propertiesThe association between exposure to a radio campaign on nutrition and mothers’ nutrition- and health-related attitudes and minimal acceptable diet of children 6–36 months old: a quasi-experimental trial
Appiah B, Saaka M, Appiah G, Asamoah-Akuoko L, Samman E, Forastiere L, Abu B, Yeboah-Banin A, Kretchy I, Ntiful F, Nsiah-Asamoah C, Ahmed K, France C. The association between exposure to a radio campaign on nutrition and mothers’ nutrition- and health-related attitudes and minimal acceptable diet of children 6–36 months old: a quasi-experimental trial. Public Health Nutrition 2024, 27: e232. PMID: 39569901, PMCID: PMC11645118, DOI: 10.1017/s1368980024001319.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsMinimum acceptable dietHealth-related attitudesIntervention districtsRadio campaignMother-child dyadsTrained community health workersAcceptable dietCommunity health workersPresence of food insecurityDifference-in-differencesQuasi-experimental trialNutrition-related attitudesControl districtsMinimal acceptable dietPre-post designLow-resource settingsChildren Aged 6Health workersIntervention effectsCommunication interventionsChild nutritionFood insecurityChildren 6Aged 6InterventionUsing overlap weights to address extreme propensity scores in estimating restricted mean counterfactual survival times
Cao Z, Ghazi L, Mastrogiacomo C, Forastiere L, Wilson F, Li F. Using overlap weights to address extreme propensity scores in estimating restricted mean counterfactual survival times. American Journal Of Epidemiology 2024, 194: 2402-2411. PMID: 39489504, PMCID: PMC12342919, DOI: 10.1093/aje/kwae416.Peer-Reviewed Original ResearchCitationsConceptsInverse probability of censoring weightingProbability of censoring weightingOverlap weightingCensoring processVariance estimationInterval coverageInverse probability of treatment weightingTarget estimandInverse probabilityBinary outcomesPropensity scoreRMSTProbability of treatment weightingPropensity score weightingEstimationEstimandsLogistic regressionTreatment comparisonsVarianceMaternal and child health intervention to promote behaviour change: a population-level cluster-randomised controlled trial in Honduras
Oles W, Alexander M, Negron R, Nelson J, Iriarte E, Airoldi E, Christakis N, Forastiere L. Maternal and child health intervention to promote behaviour change: a population-level cluster-randomised controlled trial in Honduras. BMJ Open 2024, 14: e060784. PMID: 38858139, PMCID: PMC11168147, DOI: 10.1136/bmjopen-2022-060784.Peer-Reviewed Original ResearchAltmetricMeSH Keywords and ConceptsConceptsCluster randomised controlled trialPrimary outcomeCounseling interventionChild health interventionsCommunity health workersProfessional care seekingBreast-feedingPromote behavior changeSecondary outcome measuresFacility birthsUmbilical cord careCare-seekingNewborn healthNewborn careHealth workersHealth facilitiesEndline surveyHealth interventionsEducational interventionChild healthCord careHome settingCaring behaviorsSecondary outcomesSustained educational interventions
Academic Achievements & Community Involvement
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Honors
honor Yale Global Health Spark Award
04/15/2023Yale University AwardYale Institute for Global Healthhonor Young Protagonist Researchers
11/15/2016International AwardEnte Cassa di Risparmio FirenzeDetailsItalyhonor Student Paper Award
08/03/2016National AwardHealth Policy Statistics Section - American Statistical AssociationDetailsUnited Stateshonor Thomas R. Ten Have Award
05/21/2014National AwardAtlantic Causal Inference ConferenceDetailsUnited States
News
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News
- September 03, 2024
Ariel Chao: a first in YSPH Biostatistics
- November 28, 2019
New Faculty Friday: Laura Forastiere, methodologist, statistician, world traveler
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