> Vastly more data generally trumps better analysis.
If you mean more data in the sense of more covariates, sure. If you mean more data in terms of just more observations, no way. Not when it comes to causal inference.
If you mean more data in the sense of more covariates, sure. If you mean more data in terms of just more observations, no way. Not when it comes to causal inference.
In this particular case, that commenter said that conference dates are independent enough from patient health to say that aside from the important things, everything else is equal. As in, the only difference between the treatments is who is doing the treatment. I disagree with that assertion, but that's the kind of argument you need for causation: Once we've dealt with/controlled for XYZ, the only difference left is the one we're interested in.
It's very difficult to demonstrate that the only difference left is what you care about when you can't even see certain variables. Someone says "what if people in group A are more likely to be left handed and it's patient handedness instead of doctor quality that's causing death here" but you didn't measure left handedness. More and more observations of group A without measuring handedness can't rule out that maybe they were lefties. So you either measure handedness, or argue that your setup will even it out (via randomization for example), or argue that handedness just doesn't matter here. And not just for handedness, but for everything imaginable.
Sampling always loses out to having the entire population.
But you say missing data makes the interpretation incorrect. That’s always possible but only in ways that also fit the original data. Thus, you can then preform a study that improves upon the understanding of the correlation, but it’s not going to disprove the original correlation.
PS: And even here it’s easy to test with more data, just compare total deaths for the month around conferences.