[whatshisface]

The parent is not saying the author made an incorrect claim. They are saying that the parent did not continue their argument to arrive at a conclusion that someone else had, the conclusion that causal models are what tells you when you can combine datasets and when you can't.

[chii]

> causal models are what tells you when you can combine datasets and when you can't.

but then the causal model is subjective right? What if there are two different causal models, and a priori cannot be known which is the "true" one?

Can the selection of the causal model be used to justify the dataset, in order to push a particular agenda?

[whatshisface]

Your job when analysing data is simply to enumerate the possibilities and assign likelihoods to them if possible. If two models fit equally well, you're supposed to write them both down in the hope that someone will collect further data to distinguish between them.

If you're cutting holes in your report for political reasons, that's just not doing the job. That's what pundits are paid to do, not (ideally at least) scientists. Fraud is easy to commit, and the fact that it's possible is not that hard of a philosophical issue.

[chii]

How do you tell that a paper containing conclusions to support an agenda is written with correct scientific rigor, rather than fraud? Using Simpson's paradox, one can obfuscate their biases by making the desired conclusion drop out of the data.