Bradford-Hill indicators of causality, showing the importance of both probabilistic evidence (eg, from RCTs) and mechanistic evidence (eg, from laboratory studies)
| Indicator | Explanation | Type | Preferred types of evidence |
| Strength of association | A strong association is more likely to be causal than a weak one | Both | RCTs or unbiased observational studies (eg, longitudinal cohort) which allow calculation of a measure of association such as relative risk or OR, and adjustment for confounding. |
| Consistency | Multiple observations made by different observers with different instruments mean the association is more likely to be causal | Both | Systematic review. Exploration of heterogeneity via tools such as meta-regression may help identify and quantify inconsistency in effects across studies. |
| Specificity | If an outcome is best predicted by one primary factor, the causal claim is more credible | Neither | A problematic indicator, as it is common for a single exposure to be causally associated with multiple outcomes (eg, tobacco smoking and heart disease, stroke, cancer, etc). |
| Temporality | A cause must precede an effect | Both | Any longitudinal design for example, clinical trial, non-cross-sectional observational study. |
| Biological gradient | There should be a direct ‘dose-response’ relationship between the independent variable (eg, a risk factor) and the dependent one (eg, people’s status on the disease variable) | Both | Basic science (eg, toxicology-based risk analysis), varying-dose RCTs, unbiased observational studies with adjustment for confounding. |
| Plausibility | An association is more likely to be causal if there is a rational and theoretical basis for it | Mechanistic | Basic science |
| Coherence | An association is more likely to be causal if it coheres with other knowledge (ie, does not conflict with what is known about the variables under study and there are no plausible competing theories or rival hypotheses) | Both | Systematic review; integration of both mechanistic AND probabilistic evidence is key here. |
| Experimental manipulation | Any related research that is based on experiments will make a causal inference more plausible | Both | Basic science or RCT (which may be considered ‘epidemiological experiments’, as exposure is defined by the investigator). |
| Analogy | Sometimes a commonly accepted phenomenon in one area can be applied to another area | Both | Requires a broad understanding of all relevant fields; potentially subject to logical fallacy. |
Adapted from Hill and Bradford Hill,15 incorporating important subsequent insights from Russo and Williamson16 and Fedak et al. 17
RCT, randomised controlled trial.