Thanks for the suggestion. We made an effort to be brief, but perhaps we went too far. In our paper Reasoning about causality in games, we have a longer discussion about probabilistic, causal, and structural models (in Section 2), and Pearl’s book A Primer also offers a more comprehensive introduction.
I agree with you that causality offers a way to make out-of-distribution predictions (in post number 6, we plan to go much deeper into this). In fact, a causal Bayesian network is equivalent to an exponentially large set of probability distributions, where there is one joint distribution $P_{\do(X=x)}$ for any possible combinations of interventions $X=x$.
We’ll probably at least add some pointers to further reading, per your suggestion. (ETA: also added a short paragraph near the end of the Intervention section.)
Thanks for the suggestion. We made an effort to be brief, but perhaps we went too far. In our paper Reasoning about causality in games, we have a longer discussion about probabilistic, causal, and structural models (in Section 2), and Pearl’s book A Primer also offers a more comprehensive introduction.
I agree with you that causality offers a way to make out-of-distribution predictions (in post number 6, we plan to go much deeper into this). In fact, a causal Bayesian network is equivalent to an exponentially large set of probability distributions, where there is one joint distribution $P_{\do(X=x)}$ for any possible combinations of interventions $X=x$.
We’ll probably at least add some pointers to further reading, per your suggestion. (ETA: also added a short paragraph near the end of the Intervention section.)