3 ms·

The article is a little dense for me, and presumes knowledge about Probabilistic Graphical Models (PGMs) and directed acyclic graphs / causal Bayesian networks

The article is a little dense for me, and presumes knowledge about Probabilistic Graphical Models (PGMs) and directed acyclic graphs / causal Bayesian networks (DAGs) without introducing the background knowledge - so, I expect my reading of it missed a lot of the details. But the one thing I came out wondering, is if you do a proper randomized double-blinded study with a large population sample, say, taking 1000 people with a particular illness, completely randomizing them, and then giving 500 of the sample a particular treatment, and 500 of the sample a placebo that is indistinguishable from the treatment. If an unbiased third-party, then observes the groups (still without knowledge of which group had which treatment), and if one group shows markedly different results (say, 490/500 of the treatment group recover, and only 10/500 placebo group recover), is it fair to say that in this particular scenario, that correlation of recovery with the treatment implied that the treatment caused the recovery?

mjmahone17 12y ago
Yes. In this case, you have two outcomes: Recovering and Not Recovering. You also have two starting states: Drug and Placebo (which both came off of the original starting state, Participant in Study, with a 50% probability). So your Drug -> Recover has a 98% probability, vs. Drug -> Not Recover of 2%. Likewise, Placebo -> Recover has only a 2% probability, vs. Placebo -> Not Recover of 98%. In this case, you know with very high probability that the fork you stuck in the road caused two very clear, disparate outcomes. Especially because you actually began the "give drug" phase. However, usually medical trials have results more along the lines of: Placebo group of 35 participants: 10 recovered in < 5 days, 12 recovered in < 10 days, and 13 failed to recover. Drug group of 35 participants: 17 recovered in < 5 days, 13 recovered in < 10 days, and 5 failed to recover. With data like that, clearly SOMETHING causes people to recover, and clearly your drug is not the underlying cause of recovery. It may be supporting whatever process is causing recovery, but it's very unclear whether that's even the case.
aaron695 12y ago
Well it's fair to say that experiment shows causation but no I don't think you can say the correlation shows it works. I think it goes against the definition of the word..... I found the article too dense to get as well, but I do think 'perhaps' we could use correlation more to assume causation. I think we are too cautious and certainly the haters always bring in correlation to stop science articles they think don't follow their beliefs. Not sure if that's compatible with the OP or not, it hurt my head.
- mattfenwick 12y ago
  > it's fair to say that experiment shows causation No, that experiment doesn't show causation. > I do think 'perhaps' we could use correlation more to assume causation. I think we are too cautious Why do you think that? Is it because you think that science is moving too slowly (thus wasting time and money)? Is it because you think that good results are mistakenly discarded? "using correlation more to assume causation" should produce results more quickly, both correct and incorrect. Do you understand the costs of incorrect results? First, incorrect results can have negative consequences for the consumers: for example, drugs that have harmful impacts and no beneficial ones for the people taking them (in addition to the cost of the drug, and the opportunity cost of not doing something else known to be beneficial). Second, time is wasted following up incorrect results and work based on incorrect results would be worthless. Third, the incorrect results would have to be overturned (and this communicated to everybody who had taken them as correct). I think deciding whether to "use correlation more to assume causation" needs to take into account the costs and benefits of incorrect results, as well as the costs and benefits of correct results. Do you have any data on this? (unfortunately, I do not) > certainly the haters always bring in correlation to stop science articles they think don't follow their beliefs Can you support this accusation with examples of it happening to articles that actually do a good job of showing their result is something more than a correlation? Or are you saying that this happened to articles that just present a correlation?

mjmahone17 12y ago

Yes. In this case, you have two outcomes: Recovering and Not Recovering. You also have two starting states: Drug and Placebo (which both came off of the original starting state, Participant in Study, with a 50% probability). So your Drug -> Recover has a 98% probability, vs. Drug -> Not Recover of 2%. Likewise, Placebo -> Recover has only a 2% probability, vs. Placebo -> Not Recover of 98%. In this case, you know with very high probability that the fork you stuck in the road caused two very clear, disparate outcomes. Especially because you actually began the "give drug" phase. However, usually medical trials have results more along the lines of: Placebo group of 35 participants: 10 recovered in < 5 days, 12 recovered in < 10 days, and 13 failed to recover. Drug group of 35 participants: 17 recovered in < 5 days, 13 recovered in < 10 days, and 5 failed to recover. With data like that, clearly SOMETHING causes people to recover, and clearly your drug is not the underlying cause of recovery. It may be supporting whatever process is causing recovery, but it's very unclear whether that's even the case.

aaron695 12y ago

Well it's fair to say that experiment shows causation but no I don't think you can say the correlation shows it works. I think it goes against the definition of the word..... I found the article too dense to get as well, but I do think 'perhaps' we could use correlation more to assume causation. I think we are too cautious and certainly the haters always bring in correlation to stop science articles they think don't follow their beliefs. Not sure if that's compatible with the OP or not, it hurt my head.

mattfenwick 12y ago
> it's fair to say that experiment shows causation No, that experiment doesn't show causation. > I do think 'perhaps' we could use correlation more to assume causation. I think we are too cautious Why do you think that? Is it because you think that science is moving too slowly (thus wasting time and money)? Is it because you think that good results are mistakenly discarded? "using correlation more to assume causation" should produce results more quickly, both correct and incorrect. Do you understand the costs of incorrect results? First, incorrect results can have negative consequences for the consumers: for example, drugs that have harmful impacts and no beneficial ones for the people taking them (in addition to the cost of the drug, and the opportunity cost of not doing something else known to be beneficial). Second, time is wasted following up incorrect results and work based on incorrect results would be worthless. Third, the incorrect results would have to be overturned (and this communicated to everybody who had taken them as correct). I think deciding whether to "use correlation more to assume causation" needs to take into account the costs and benefits of incorrect results, as well as the costs and benefits of correct results. Do you have any data on this? (unfortunately, I do not) > certainly the haters always bring in correlation to stop science articles they think don't follow their beliefs Can you support this accusation with examples of it happening to articles that actually do a good job of showing their result is something more than a correlation? Or are you saying that this happened to articles that just present a correlation?