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?
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?