Hill’s Considerations Are Not Causal Criteria
Hill’s list of considerations for assessing causality, proposed 60 years ago, became a landmark in the interpretation of epidemiologic evidence. However, it has been and continues to be misused as a list of causal criteria to be scored and summed, despite causal inference being unattainable through...
Uloženo v:
| Vydáno v: | Journal of clinical epidemiology s. 112087 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
Elsevier Inc
22.11.2025
|
| Témata: | |
| ISSN: | 0895-4356, 1878-5921, 1878-5921 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Shrnutí: | Hill’s list of considerations for assessing causality, proposed 60 years ago, became a landmark in the interpretation of epidemiologic evidence. However, it has been and continues to be misused as a list of causal criteria to be scored and summed, despite causal inference being unattainable through the application of this or any other algorithm. Recognizing the distinction between statistical associations and causal effects was a key contribution of Hill. While he identified several clues for distinguishing between causal and non-causal associations, causal inference in epidemiology has become much more explicit and effective. Rather than relying on Hill’s indirect hints of potential bias by considering strength of association or dose-response gradients, newer methods such as quantitative bias analysis directly assess confounding and other candidate biases that compete with causal explanations, leading to more informed inferences. Similarly, the interpretation of consistency depends on variation in methods across studies; triangulation may be used to search for informative inconsistencies, strengthening causal inference. Most importantly, a causal connection is not a categorical property bestowed upon an association based on Hill’s considerations or any other checklist. Causal inference is an inherently indirect process, with the inference gradually crystallizing by withstanding challenges from competing theories in which other explanations, including random error or biases, are found not to account for the measured association.
•Hill’s considerations have been misused as a checklist to certify a conclusion of causality•Epidemiologic methods for assessing causality have advanced considerably after Hill’s publication•Causal inference is not based on an algorithm but is a tentative explanation for an association, balancing evidence from competing candidate explanations |
|---|---|
| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 0895-4356 1878-5921 1878-5921 |
| DOI: | 10.1016/j.jclinepi.2025.112087 |