Remember when a glass of wine a day was good for you?

Of course there is great research being done. The problem is that the cognitive load of evaluating which research is useful has been shifted to the end "consumers" of that research.

Not the best way to get your ad revenue up.

How would one really prove causality or does it even matter. When I think of science, I try to think of it like how I think of physics, where we create a model that best describes the evidence but which doesn't have any guarantee of being how things really work.

Take Newtonian gravity. It is a pretty good model that describes a lot of basic interactions. Given a state at a given moment in time, it lets us determine things going forward or backwards (though for more complex physics, backwards stops working because of assumptions and estimates in the model). But at the same time, it is wrong. More complex physics shows there is a model that fits even more experimental data which contradicts what we thought was happening in the Newtonian model. Mass doesn't attract mass. Mass bends space time and impacts objects traveling through it in such a way that it appears mass attracts mass (though even this may end up being wrong and something else entirely is at play).

So, is it really important to describe why a ball falls to the ground when I release it, or is it good enough to have a system that describes the interactions enough that I can apply it to problems? If I solve a problem, say what angle and what speed I need to throw a ball to get over fence, does it really matter if I think the ball falls back to earth because mass attracts mass or because mass bends spacetime? Or is it just important for my equations to be accurate enough that any error is less than what is innate in applying the solution to real life (measuring the exact height of the fence, throwing at an exact angle).

What does applying a similar mind set in something dealing with vastly more complicated systems, such as in medicine, looks like?

Does cataract surgery reduce mortality in older women? It sure looked like it, until it was recognized that the study suffered from a concept called "immortal time bias" [1]. Now the reverse "seems" to be true. (Unhelpfully, it's easy to make an "explanation" for either case. Surgery lowers mortality? Of course! Better vision keeps you safe. Surgery increases mortality? Of course! Failing vision is associated with other failing organs, and surgery itself poses risks.)

In cardiology, we like to refer to immortal time bias as the "cheetos" effect. An example: Let's look at people who had an event (heart attack) and try to estimate the benefit of a procedure done afterward (literally eating a bag of cheetos within 60 days after the heart attack). You will find that eating a bag of cheetos confers increased survival compared to not eating a bag of cheetos. The reason for this is that in order to have eaten the bag of cheetos, you necessarily lived long enough to eat the cheetos. If you died first, you count toward the non-cheetos group. So stacking things up, it will look like cheetos keeps you alive longer. (The solution to this problem is not to count the time before eating cheetos towards your survival time. If you do this with literal cheetos, which don't have a survival effect, then you will correctly see no difference between groups.)

This is just one of the numerous biases that exist and can be difficult to account for. In my opinion, this stuff is just hard. If you are interested, Judea Pearl is developing tools for causal inference [2].

1 = https://retractionwatch.com/2018/08/28/a-high-profile-paper-...

2 = http://bayes.cs.ucla.edu/jp_home.html