I don’t think I had seen that, and wow, it definitely covers basically all of what I was thinking about trying to say in this post, and a bit more.
I do think there is something useful to say about how reference class combinations work, and using causal models versus correlational ones for model combination given heterogeneous data—but that will require formulating it more clearly than I have in my head right now. (I’m working on two different projects where I’m getting it straighter in my head, which led to this post, as a quick explanation why people need to stop using “reference classes” that don’t work well because they can’t find a better one, as if “reference class” is an argument about correctness of a prediction.)
Previously: Model Combination and Adjustment.
I don’t think I had seen that, and wow, it definitely covers basically all of what I was thinking about trying to say in this post, and a bit more.
I do think there is something useful to say about how reference class combinations work, and using causal models versus correlational ones for model combination given heterogeneous data—but that will require formulating it more clearly than I have in my head right now. (I’m working on two different projects where I’m getting it straighter in my head, which led to this post, as a quick explanation why people need to stop using “reference classes” that don’t work well because they can’t find a better one, as if “reference class” is an argument about correctness of a prediction.)