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Improving causal determination
Improving causal determination
- Source :
- Global Epidemiology, Vol 1, Iss , Pp - (2019)
- Publication Year :
- 2019
- Publisher :
- Elsevier, 2019.
-
Abstract
- Holistic expert judgments about causality are widely used in regulatory risk assessments, with causal determination categories being used to summarize huge amounts of complex evidence and to help inform and drive major regulatory decisions. 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. We note fundamental limitations of such procedures. 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. “Causal relationship” is usually left undefined, and may be interpreted very differently by different people. Whether it refers to direct, indirect, or total causal effects is seldom specified. We propose that causal partial dependence plots 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.
Details
- Language :
- English
- ISSN :
- 25901133
- Volume :
- 1
- Issue :
- -
- Database :
- Directory of Open Access Journals
- Journal :
- Global Epidemiology
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.4d89108a103c4faa96cc88ed3b700cd9
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.gloepi.2019.100004