Thank for taking time to answer my question, as someone from the field!
The links you’ve given me are relevant to my question, and I can now rephrase my question as “in general, if we observe two things aren’t correlated, how likely is it that one influences the other”, or, simpler, how good is absence of correlation as evidence for absence of causation.
People tend to give examples of cases in which the absence of correlation goes hand in hand with the presence of causation, but I wasn’t able to find an estimate of how often this occurs, which is potentially useful for the purposes of practical epistemology.
I want to push back a little bit on this simulation being not valuable—taking simple linear models is a good first step, and I’ve often been surprised by how linear things in the real world often are. That said, I chose linear models because they were fairly easy to implement, and wanted to find an answer quickly.
And, just to check: Your second and third example are both examples of correlation without causation, right?
I want to push back a little bit on this simulation being not valuable—taking simple linear models is a good first step, and I’ve often been surprised by how linear things in the real world often are. That said, I chose linear models because they were fairly easy to implement, and wanted to find an answer quickly.
I was thinking more of the random graphs. It’s a bit like asking the question, what proportion of yes/no questions have the answer “yes”?
It’s a bit like asking the question, what proportion of yes/no questions have the answer “yes”?
Modus ponens, modus tollens: I am interested in that question, and the answer (for questions considered worth asking to forecasters) is ~40%.
But having a better selection of causal graphs than just “uniformly” would be good. I don’t know how to approach that, though—is the world denser or sparser than what I chose?
Thank for taking time to answer my question, as someone from the field!
The links you’ve given me are relevant to my question, and I can now rephrase my question as “in general, if we observe two things aren’t correlated, how likely is it that one influences the other”, or, simpler, how good is absence of correlation as evidence for absence of causation.
People tend to give examples of cases in which the absence of correlation goes hand in hand with the presence of causation, but I wasn’t able to find an estimate of how often this occurs, which is potentially useful for the purposes of practical epistemology.
I want to push back a little bit on this simulation being not valuable—taking simple linear models is a good first step, and I’ve often been surprised by how linear things in the real world often are. That said, I chose linear models because they were fairly easy to implement, and wanted to find an answer quickly.
And, just to check: Your second and third example are both examples of correlation without causation, right?
Yes, I broadened the topic slightly.
I was thinking more of the random graphs. It’s a bit like asking the question, what proportion of yes/no questions have the answer “yes”?
Modus ponens, modus tollens: I am interested in that question, and the answer (for questions considered worth asking to forecasters) is ~40%.
But having a better selection of causal graphs than just “uniformly” would be good. I don’t know how to approach that, though—is the world denser or sparser than what I chose?