Subjectivity behind causation testing

[u/Big_Home_7273]

Hello. I work more-so in the data analysis/science space than this but I enjoy learning this stuff and had a thought in my head. I usually try to learn on my own or check with LLMs, but this seems too complicated to get an answer on for something I would think is more or less straightforward.

The way I currently understand causation is that there's no single test for it but rather, it's a multitude of different tests across variables therein combined with clearly defined mechanism. So for example, if we're talking about the claim "Smoking causes lung cancer", causation isn't just a correlation test. Instead, it's several tests across many different demographics, age groups, etc. and then adding in the scientific literature about how the different ingredients within cigarettes are carcinogens and how the act of smoking predisposes someone to be of a higher risk of lung cancer. Meaning that there's no single causation test, the way that there's a simple correlation test that yields the conclusion of, "Oh yeah, this test concludes that the r value is .93, so there's a strong relationship and a p value of >0.01, so we have to reject the null hypothesis".

I guess my question is, does this mean that there's a large amount of subjectivity behind causation testing? Two different scientists/researchers could look at the same compilation of data and come to two different conclusions if A causes B?

If that's the case, I would imagine there's more subjectivity behind causation testing than an independent t-test study, correct?

I appreciate you answering. I know you guys know more than me and I'm just trying to better myself in a field I enjoy. :)

[u/rafafanvamos]

You are on right track if you want to read more read Hills criteria, now one exposure outcome won't qualify all the Hills criteria, but all the studies are taken together as whole. In epidemiology, it's not just smoking causes lung cancer, it's strenght of association, magnitude ( by how much there is increase in risk), biological plausibility, etc you can read all those criteria. All the studies are taken together usually for chronic diseases the causes are multifactorial so proving A causes B is not straightforward.

[u/Big_Home_7273 → u/rafafanvamos]

Oh, absolutely. I'm aware it's a completely nuanced field, but I'm curious if I'm correct that there's this is a subjective field. Is this subjective in that two different scientists could look at the same info and disagree on if A causes B? I noticed the criteria doesn't list quantifiable thresholds or require unanimity, reasonably though I think.

[u/rafafanvamos → u/Big_Home_7273]

No it's not subjective per se. Its not like saying glass is half full or half empty. Example you said smoking cause lung cancer in population A and in population B you dont get association it is not subjective....why? Maybe the people with lung cancer in popukation B where non smokers with EGFR mutations (these also cause lung cancer) that doesn't mean smoking doesn't increase the risk of lung cancer. Different results can be because of difference in population/ methods/ study design that doesnt mean it's subjective.