the data comes from the territory, but we assume the map is correct.
You don’t need any assumptions about the model to get observational data. Well, you need some to recognize what are you looking at, but certainly you don’t need to assume the correctness of a causal model.
no longer purely a prediction model as everyone in the ML field understands it
We may be having some terminology problems. Normally I call a “prediction model” anything that outputs testable forecasts about the future. Causal models are a subset of prediction models. Within the context of this thread I understand “prediction model” as a model which outputs forecasts and which does not depend on simulating the mechanics of the underlying process. It seems you’re thinking of “pure prediction models” as something akin to “technical” models in finance which look at price history, only at price history, and nothing but the price history. So a “pure prediction model” would be to you something like a neural network into which you dump a lot of more or less raw data but you do not tweak the NN structure to reflect your understanding of how the underlying process works.
Yes, I would agree that a prediction model cannot talk about counterfactuals. However I would not agree that a prediction model can’t successfully forecast on the basis of inputs it never saw before.
So are you willing to take me up on my offer of solving causal problems with a prediction algorithm?
Good prediction algorithms are domain-specific. I am not defending an assertion that you can get some kind of a Universal Problem Solver out of ML techniques.
You don’t need any assumptions about the model to get observational data. Well, you need some to recognize what are you looking at, but certainly you don’t need to assume the correctness of a causal model.
We may be having some terminology problems. Normally I call a “prediction model” anything that outputs testable forecasts about the future. Causal models are a subset of prediction models. Within the context of this thread I understand “prediction model” as a model which outputs forecasts and which does not depend on simulating the mechanics of the underlying process. It seems you’re thinking of “pure prediction models” as something akin to “technical” models in finance which look at price history, only at price history, and nothing but the price history. So a “pure prediction model” would be to you something like a neural network into which you dump a lot of more or less raw data but you do not tweak the NN structure to reflect your understanding of how the underlying process works.
Yes, I would agree that a prediction model cannot talk about counterfactuals. However I would not agree that a prediction model can’t successfully forecast on the basis of inputs it never saw before.
Good prediction algorithms are domain-specific. I am not defending an assertion that you can get some kind of a Universal Problem Solver out of ML techniques.