title: Causal inference in cumulative risk assessment: The roles of directed acyclic graphs.
abstract: Cumulative risk assessments (CRAs) address exposures to multiple chemical and nonchemical stressors and often focus on characterization of health risks in vulnerable populations. Evaluating complex exposure-response relationships in CRAs requires the use of formal and rigorous methods for causal inference. Directed acyclic graphs (DAGs) are graphical causal models used to organize and communicate knowledge about the underlying causal structure that generates observable data. Using existing graphical theories for causal inference with DAGs, risk analysts can identify confounders and effect measure modifiers to determine if the available data are both internally valid to obtain unbiased risk estimates and are generalizable to populations of interest. Conditional independencies implied by the structure of a DAG can be used to test assumptions used in a CRA against empirical data in a selected study and can contribute to the evidence evaluations related to specific causal pathways. This can facilitate quantitative use of these data, as well as help identify key research gaps, prioritize data collection activities, and evaluate risk management alternatives. DAGs also enable risk analysts to be explicit about sources of uncertainty and to determine whether a causal effect can be estimated from available data. Using a conceptual model and DAG for a hypothetical community located near a concentrated animal feeding operation (CAFO), we illustrate the advantages of using DAGs for evaluating causality in CRAs. DAGs also can be used in conjunction with weight of evidence (WOE) methodology to improve causal analysis for CRA, which could lead to more effective interventions to reduce population health risks.
authors.affiliation: Oak Ridge Institute for Science and Education (ORISE), U.S. Environmental Protection Agency, Office of Research and Development, Office of the Science Advisor, 1300 Pennsylvania Ave., NW, MC8195R, Washington, DC 20004, United States. Electronic address: brewer.beth@epa.gov.
authors.forename: L Elizabeth
authors.initials: LE
authors.lastname: Brewer
authors.affiliation: U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, 26 W. Martin Luther King Dr., MS-A110, Cincinnati, OH 45268, United States. Electronic address: wright.michael@epa.gov.
authors.forename: J Michael
authors.initials: JM
authors.lastname: Wright
authors.affiliation: U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, 26 W. Martin Luther King Dr., MS-A110, Cincinnati, OH 45268, United States.
authors.forename: Glenn
authors.initials: G
authors.lastname: Rice
authors.affiliation: U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, B305-01, Research Triangle Park, NC 27711, United States.
authors.forename: Lucas
authors.initials: L
authors.lastname: Neas
authors.affiliation: LK Teuschler and Associates, St. Petersburg, FL 33707, United States.
authors.forename: Linda
authors.initials: L
authors.lastname: Teuschler
mesh_terms: D015984:Causality; D003196:Computer Graphics; D015986:Confounding Factors, Epidemiologic; D003625:Data Collection; D003627:Data Interpretation, Statistical; D004781:Environmental Exposure / Q000032:analysis* / Q000706:statistics & numerical data; D004785:Environmental Pollutants / Q000032:analysis*; D006801:Humans; D008962:Models, Theoretical; D018570:Risk Assessment / Q000379:methods* / Q000706:statistics & numerical data; D035501:Uncertainty
chemical_list: D004785:Environmental Pollutants
keywords: Causal inference; Causal models; Conceptual model; Confounding; Cumulative risk assessment; Directed acyclic graph