There is something really special about the randomization process and the theoretical balancing of known and unknown confounders.

** But Noetsi**, I bet you didn't know the following about randomized/experimental studies.

Randomization studies have their own issues. Of primary interest is the adherence to treatment assignment. You can tell a person they are in a group, but you always have the risk of never takers and treatment defiers, and whether these deviations are related to the outcome. In addition, you can have differential loss to follow and many other issues like interference (one person's treatment affects the outcome of another person) and contamination (one person's outcome affects the outcome of another person).

Guess what, this is where the exact same observational causal approaches get applied to randomization studies and these things go beyond intent-to-treat protocols. In addition, the experimental studies can now have some of the exact same flaws as the observational studies, such as correlation in error terms.

So imagine the key causality assumptions: exogeneity, no multiple versions of interventions, probability of being in either treatment group (0 < probability < 1), interference, no measurement error, and model misspecification are all of a sudden in play and the analyst needs to apply greater assumptions into their statistical approaches.

This is why that one time I said animals and plants can't opt out of studies. Pretty much the majority of studies we would be interested in are at risk of the above topics, and the big follow-up kicker would be that results from human analogs should not be assumed to translate into the same results you would see in humans. This is in regards to animal and plant studies. Also there are two relevant other sayings, kids aren't little adults, and middle age people aren't the same as the elderly - meaning results from these groupings also may not translate over and run into extrapolation problems. I have yet to see a randomized study that did not have protocol application issues or deviations. Meaning, everyone assumes they are the best and without flaws, but this is not always the case.