A causal machine-learning framework for studying policy impact on air pollution: a case study in COVID-19 lockdowns
Heffernan C, Koehler K, Zamora M, Buehler C, Gentner D, Peng R, Datta A. A causal machine-learning framework for studying policy impact on air pollution: a case study in COVID-19 lockdowns. American Journal Of Epidemiology 2024, kwae171. PMID: 38960671, DOI: 10.1093/aje/kwae171.Peer-Reviewed Original ResearchComparative interrupted time seriesAir pollutionCausal effectsAir pollution time seriesPollution time seriesTime seriesImpact of policy interventionsEnvironmental policyEastern USNatural experimentPolicy impactPolicy interventionsEmpirical validationPollutionIndustrial facilitiesFalse effectsBaseline yearNO2PolicyImpact of COVID-19Interrupted time series