BIO 550 Week 6 DQ 2: Are Weak Associations Causal?

The answer is no, and the reason a straightforward no is not sufficient is that the question is more subtle than the previous week's. Strength of association is one of the Bradford Hill viewpoints, so a weak association is genuinely weaker evidence — but weaker evidence is not evidence of absence, and treating it that way inverts what the viewpoints were for. Hill himself was explicit that these were considerations to be weighed rather than tests to be passed, and strength was never proposed as necessary. So the position to defend is that a weak association neither establishes nor refutes causation; it shifts how much other evidence you need before concluding anything. Say that clearly, then spend the post on the two things that make it interesting. Both of them are empirical rather than logical, which is why this question rewards examples more than reasoning.

The first is that real causal relationships with small effect sizes are common and well documented, so the claim can be settled by counterexample rather than by argument. Environmental tobacco smoke and lung cancer produces a modest relative risk and is accepted as causal. Many dietary exposures, low-dose environmental contaminants, and common genetic variants operate at effect sizes far below those of the classic examples. If small effects were reliably noncausal, none of these findings could stand, and a great deal of what public health acts on would be unfounded. One or two documented cases make this argument better than any amount of reasoning about the logic of inference. Give the actual effect size where you can, since a counterexample with a number attached is much harder for a classmate to wave away than one named in passing.

The second, and the one that makes the prompt worth asking, is why weak associations are harder to defend even though they can be true. A small association is more easily produced by something other than a causal effect. An unmeasured confounder needs only a modest relationship with both exposure and outcome to generate a relative risk of 1.2, whereas producing a relative risk of 10 spuriously would require a confounder so strong it would almost certainly already be known. The same holds for differential misclassification and for selection effects, which distort weak signals proportionally far more than strong ones. So the correct statement is not that weak associations are noncausal but that they are less robust to the alternative explanations you learned last week. That is a claim about how much scrutiny a finding needs, not about whether it is true, and keeping those two apart is the whole discipline of this question.

That asymmetry has a practical consequence worth stating, because it is what a working epidemiologist actually does with the principle. When an association is weak, the burden falls on the other viewpoints: consistency across different populations and study designs, a dose-response gradient, biological plausibility, and evidence from designs less vulnerable to confounding. A weak association replicated in a dozen settings with a clear gradient is more persuasive than a strong association seen once. This reframes strength as one input to a weight-of-evidence judgement rather than as a threshold, which is exactly how the framework was intended to be used. Triangulation is the useful word here: several designs with different weaknesses converging on the same answer is stronger evidence than one design producing a large number. A single striking result is exactly what a systematic bias would also produce, which is why replication across methods carries the weight.

Then make the public health argument, since this is a public health course and the point transforms the question. A weak association affecting an entire population can produce more disease than a strong association affecting very few people. That is the logic of population attributable fraction: impact depends on both the strength of the effect and the prevalence of the exposure. Air quality, dietary sodium, and sedentary behaviour all illustrate it — modest individual effects, enormous aggregate burden. So a weak association may deserve more attention from a health department than a strong one, which stands the prompt's implication neatly on its head and is the strongest single contribution you can make to this discussion. It also connects the methodological question to the operational one, which is what a course in epidemiology for public health professionals is ultimately for.

Finally, keep the terminology honest, because this topic tempts overstatement in both directions. Weak and strong are not defined thresholds, and what counts as a small effect differs by field and by outcome. Statistical significance is not strength: a very large study can produce a highly significant relative risk of 1.05, and a small one can miss a real relative risk of 2. Be careful too not to over-correct into claiming that effect size is irrelevant, since it plainly is evidence. Sourcing matters here, because both the counterexamples and the confounding argument are empirical claims rather than positions, and a post that asserts them without citation has demonstrated the opposite of the appraisal skill the week is teaching. Cite the effect sizes and cite the confounding argument, and the post becomes difficult to argue with rather than merely reasonable.