As part of its responsibilities under the Clean Air Act, the U.S. Environmental Protection Agency sets National Ambient Air Quality Standards (NAAQS) for the air pollutants carbon monoxide, lead, oxides of nitrogen, particulate matter, ozone, and sulfur dioxide. EPA uses a "weight of evidence approach" to evaluate evidence from scientific studies and describe the causal relationships between these "criteria pollutants" and any adverse impacts on human health and on public welfare - including impacts on wildlife, water, forests, agriculture, and climate. The evaluation, called an Integrated Science Assessment, is used to inform standards setting associated with the criteria pollutants. This report, produced at the request of EPA, describes EPAs and several other frameworks for inferring causality of health or welfare effects and the characteristics of evidence useful for forming a causal determination. The report concludes that EPAs causal framework is effective, reliable, and scientifically defensible, provided that key scientific questions are identified and a range of necessary expertise is engaged. More transparency in how EPA integrates evidence could improve confidence in their determinations, and more guidance is needed in the framework on how evidence should be examined for vulnerable groups (e.g., human sub-populations) and sensitive ecosystems or species. Table of ContentsFront MatterSummary1 Introduction2 Historical and Legal Perspectives on the NAAQS Framework for Assessing Causality3 Definition of Causality4 Influence of the Individual Study on the Body of Evidence5 Evidence Synthesis in Integrated Science Assessments6 Example Critiques of the ISA Process and Causal Determination Framework7 Illustrative Frameworks for Causal Determinations8 Emerging Methods for Observational Studies Contributing to Causal Determinations9 Use of the Framework to Assess Both Health and Welfare Effects10 Conclusions and RecommendationsReferencesAppendix A: Committee Member BiographiesAppendix B: Open Session AgendasAppendix C: Introduction to Causal Inference Principles
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