I haven't read the whole paper yet, but so far it looks like there's no causal analysis, only associative, so anyone speculating about causality based on these

I haven't read the whole paper yet, but so far it looks like there's no causal analysis, only associative, so anyone speculating about causality based on these results is putting the cart before the horse. By far the more likely explanation for any association of this type is a common cause. For example, take this sentence from the results: > Infections during early childhood (birth to 5 years) significantly correlated with heightened risks of ADHD or ASD [...] One plausible cause of such an association is pleiotropy: a genetic variant that influences one's risk for both infection and neurodevelopmental disorders. Sadly, it is standard practice in the sciences to report "A is associated with B" using phrasings that the general public (and even many scientists) interpret to mean "A causes B". For example, this sentence from the abstract: > The findings suggest links to immunodysregulation and metabolism, compounded by stress, early-life infection, and antibiotics. Or this sentence from the results: > Penicillin use between 1 and 2.5 years resulted in a 1.6-fold (1.2–2.1, 95% CI) higher likelihood of future ASD (p = 0.0030). None of these are causal statements, but many people would read them as statements that one thing causes another. As much as I complain, though, I honestly don't know how much I can blame the scientific community for accepting "misleading" statements like this. Because it's also possible that regardless of what wording scientists settled on for expressing an association with no implication of causality, the general public would latch on to that phrase and read causality into it. Edit: I also want to mention that there's absolutely nothing wrong with associative analyses, as long as you don't try to draw conclusions about causality from them. Edit 2: Reading further into the results, they report a LOT of p-values and CIs with no adjustment for multiple testing, and many of those p-values are barely less than 0.05 and would definitely not be significant with proper multiple testing adjustment (i.e. many of them have a high likelihood of being false positives).