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Methods of Causal Analysis for Health Risk Assessment with Observational Data

In: Quantitative Risk Analysis of Air Pollution Health Effects

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  • Louis Anthony Cox Jr.

    (Cox Associates and University of Colorado)

Abstract

Perhaps no other topic in risk analysis is more difficult, more controversial, or more important to risk management policy analysts and decision-makers than how to draw valid, correctly qualified conclusions from observational data. Chapters 1 , 2 , 7 , and 8 have warned against the common practice of using statistical regression models in place of causal analysis (Pearl 2014), and have suggested some alternatives, including causal analysis and Bayesian network (BN) modeling. This chapter examines these methods in greater detail. It is a more technically dense chapter than others in this book, but the required effort pays substantial dividends in methods for more clearly assessing evidence of causality in exposure concentration-response (C-R) functions, as illustrated in the chapters that follow. Readers who are content to accept that “correlation is not causation” and who are satisfied with the high-level descriptions of Bayesian networks and causal analysis given in Chaps. 1 and 2

Suggested Citation

  • Louis Anthony Cox Jr., 2021. "Methods of Causal Analysis for Health Risk Assessment with Observational Data," International Series in Operations Research & Management Science, in: Quantitative Risk Analysis of Air Pollution Health Effects, edition 1, chapter 0, pages 219-281, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-57358-4_9
    DOI: 10.1007/978-3-030-57358-4_9
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