Improving Causal Determination

Louis Anthony Cox
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Louis Anthony Cox: Cox Associates and University of Colorado

Chapter Chapter 18 in Quantitative Risk Analysis of Air Pollution Health Effects, 2021, pp 507-523 from Springer

Abstract: Abstract Chapter 1 noted that expert judgments about causality are widely used in current regulatory risk assessment and policy making. They are often expressed within a weight-of-evidence (WoE) framework, with causal determination categories being used to summarize huge amounts of complex evidence and to help inform and drive major regulatory decisions. This chapter takes a closer look at causal determination categories and judgments. It identifies some limitations of current practices in regulatory risk assessment, and suggests possibilities for making them more useful to risk analysts and policy makers. In current practice, the causal determination categories used typically cover a relatively narrow range (e.g., from “causal relationship,” “likely to be a causal relationship,” or “suggestive of a causal relationship” to “inadequate to infer a causal relationship” and “not likely to be a causal relationship”). Other categories, such as “not a causal relationship” or “likely to not be a causal relationship,” are omitted entirely. The first part of this chapter notes that a few categories cannot encode most of the wide variations in evidence about risks and causal exposure-response relationships found in both theory and practice (Chap. 17 ). “Causal relationship” is usually left undefined, and may be interpreted very differently by different people, especially since there are several quite different technical concepts of causation (Chap. 9 ). Whether it refers to direct, indirect, or total causal effects is seldom specified. Drawing on the technical methods reviewed in Chap. 9 and illustrated in subsequent chapters, propose that causal partial dependence plots (PDPs) of predicted risk against exposure, calculated from conditional probability tables (CPTs) or models that satisfy an empirically testable condition of invariant causal prediction (ICP) across studies, can provide much more useful and clearly defined information to decision-makers. This alternative framework treats causal relationships, and evidence about them, as continuous and quantitative rather than categorical and qualitative. This is not only advantageous for clarity and realism, but it encourages better use of data and scientific method, including applying independently verifiable tests to inform conclusions about how and whether changes in exposures would change individual and population health risks.

Date: 2021
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DOI: 10.1007/978-3-030-57358-4_18

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