I would still say that cause and effect is a subset of the kind of models that are used in statistics.
You would be wrong, then. The subset relation is the other way around. Bayesian networks are not causal models, they are statistical independence models.
Compressing information has nothing to do with causality. No experimental scientist talks about causality like that, in any field. There is a big literature on something called “compressed sensing,” for example, but that literature (correctly) does not generally make claims about causality.
I’m not aware of a theory or a model that uses vastly different entities to explain and to predict.
I am.
You can’t tune (e.g. trade off bias/variance properly) causal models in any kind of straightforward way, because the parameter of interest is never unobserved, unlike standard regression models. Causal inference is a type of unsupervised problem, unless you have experimental data.
Rather than arguing with me about this, I suggest a more productive use of your time would be to just read some stuff on causal inference. You are implicitly smuggling in some definition you like that nobody uses.
You would be wrong, then. The subset relation is the other way around. Bayesian networks are not causal models, they are statistical independence models.
Compressing information has nothing to do with causality. No experimental scientist talks about causality like that, in any field. There is a big literature on something called “compressed sensing,” for example, but that literature (correctly) does not generally make claims about causality.
I am.
You can’t tune (e.g. trade off bias/variance properly) causal models in any kind of straightforward way, because the parameter of interest is never unobserved, unlike standard regression models. Causal inference is a type of unsupervised problem, unless you have experimental data.
Rather than arguing with me about this, I suggest a more productive use of your time would be to just read some stuff on causal inference. You are implicitly smuggling in some definition you like that nobody uses.