Time to dust off this oldie but goodie:
Correlation is not causation.
Correlation is not causation.
Others in the thread point out how it's not easy to implement, but there is a methodological attempt at casual inference in the design.
They didn't perform an experiment, they took someone else's observational data from a real-world event, and attempted to interpret it as if it had been an experiment.
Observational would be simply looking at sugar consumption across a cohort and having that as a covariate.
The fact someone else gathered the data has nothing to do with it.
Edit: It's a widely-used research technique, which attempts to address this correlation-causation issue without having to do RCTs.
https://medium.com/@arun.subram456/causal-inference-regressi...
Assuming their math and design are good, this is pretty strong, as these things go. But there were a lot of other things that were different between the groups that could reasonably be affecting results.
The article itself is careful to deny drawing any prescriptive conclusions from this research — a tacit acknowledgement that the causal connection isn’t entirely clear.
This is interesting and fairly strong, and potentially important, but definitely needs more work before we start acting on it.
It's not as good as an RCT, but lifetime sugar consumption, as the article points out, is not something you can randomise.