Reasoning
Two mechanisms make cohort diet–disease associations non-identifiable. (a) Confounding by correlation: almost all nutritional variables are correlated with one another, so if even one is causally related to an outcome, many correlated others will also show significant associations in large samples — significance therefore does not localise the causal component. Diet is further entangled with time-varying social/behavioral factors (a healthy-user matrix), and no available cohort measures enough of them to adjust adequately; individuals consume thousands of chemicals in millions of combinations (>250,000 foods, >500 polyphenols in seemingly similar items), so the true causal variable is typically unmeasured. Ioannidis’s genome-linkage analogy: studying single foods is like linking large chromosomal regions to disease with a handful of microsatellite markers — each region (food) contains thousands of variants (chemicals), so the design cannot resolve the causal unit. (b) Analytic multiplicity: the vast space of analyzable associations plus non-prespecified reporting means investigators can and do report a subset of many possible analyses; “meta-analyses become weighted averages of expert opinions,” and single significant slices are published without accounting for the cohort’s other findings. Under (a)+(b), obtaining significant associations for almost any food is the expected null-model behavior of the method, so such associations carry little evidential weight for causation. The inference is valid: a test whose positive rate is high under the no-causal-effect scenario (because of confounding + multiplicity) has low discriminating power, so its positives do not support causal claims.
Validity (step 6)
status: approved — reason_if_not_false: checked. Traced both legs and the join. Leg (a): if dietary variables are pervasively mutually correlated and even one is causal, correlated others acquire significant associations in large samples, so significance does not localise the causal variable; combined with the true causal unit (a specific chemical among thousands) being unmeasured and the healthy-user matrix under-adjusted, a single food’s effect is not identifiable — this follows from the correlation/omitted-variable premises. Leg (b): a large space of non-prespecified analyses with selective reporting inflates the realized positive rate. Join: under (a)+(b) the probability of a significant single-food association is high even when no single-food causal effect exists, i.e. P(positive | no effect) is high, so the likelihood ratio of a positive is near 1 and it barely discriminates — a valid Bayesian/signal-detection step. Probed for an undercutting defeater conditional on premises: “triangulation with MR/RCTs can still identify some foods” — does not break the step, because the claim is explicitly scoped to cohort single-food estimates (“in cohorts”), and appealing to non-cohort designs steps outside that scope rather than defeating the inference within it. The strong words “non-identifiable” / “carry little evidential weight” are exactly what the premises deliver, so no weakening needed. Approved as stated.