But theoretically speaking, in a scientific context, why would you want to fit an explanatory model that includes multiple highly (but not perfectly) correlated independent variables?

It shouldn't be an accident. Usually it's because you've intentionally taken multiple proxy measurements of the same theoretical latent variable and you want to reduce measurement error. So that becomes a part of your measurement and modeling strategy.

Statistical modeling is done primarily in service of scientific discovery--for the purpose of making an inference (population estimate from a sample) or a comparison to test a hypothesis derived from a theoretical causal model of a real-world process before viewing data. The parameters of a model are interpreted because they represent an estimate of a treatment effect of some intervention.

Methods like PCA can be part of that modeling process either way, but analyzing and fitting models to data to mine it for patterns without an a priori hypothesis is not science.

https://mixtape.scunning.com/01-introduction#do-not-confuse-...

Some people experience drinking-related issues other than alcoholism and I just don't want to conflate those issues with alcoholism. I know some actual alcoholics. It's different.

I used to drink 3 beers almost every night. I cut down on it to lose weight and now have a single beer 2-3 nights a week and no alcohol other nights. Some weeks I have none. If I had an actual "drinking problem" i.e. alcoholism, I wouldn't have been able to do that.

Cutting out the snacking is probably also a major factor in the effectiveness of intermittent fasting, especially given that snack foods are often more processed, calorie-dense, and less nutritionally balanced than what we typically eat at meals.

I think a lot of people could lose significant weight by simply eating three meals a day at the usual times, but just cutting out snacking between meals entirely and changing nothing else. Just fasting between meals, if you will.