Observational studies can be incredibly hard to do in a way where you can infer causality. But most of the data you can get your hands on are observational. Do

Observational studies can be incredibly hard to do in a way where you can infer causality. But most of the data you can get your hands on are observational. 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-linked-cataract-surgery-to-a-lower-risk-of-death-it-was-wrong/ https://retractionwatch.com/2018/08/28/a-high-profile-paper-... 2 = http://bayes.cs.ucla.edu/jp_home.html http://bayes.cs.ucla.edu/jp_home.html