Techniques (eg., of ML or non-ML) do not decide between explanation and prediction. It's common in ML to speak like many computer scientists do, completely ignorantly of science, and suppose somehow it is the algorithm or how we "care about" it which matters -- no.
It is entirely due to the experimental conditions which are a causal semantics on the data, not given in the data or in the algorithm -- something the experimenter or scientist will be aware of, but nothing the computer scientist will even have access to.
Regression is explanatory if the data set is causal, has been causally controlled, the data represents measures of causal properties, these measures are reliable in the experimental conditions, the variables under question each have causal relationship, and so on. Conditions entirely absent in the data and in the algorithm, and in anything to do with ML.
In a large number majority of cases where ML is applied, the data might as well be a teen survey in cosmo magazine and the line drawn an instrumental bit of pseudoscience. This is why the field is not part of scientific statistics -- because it aims to address "data as number" not "data as casual measure". The computer scientist thinks that ML can be applied to mathematics, or games like chess which is a nonsense scientifically (since there are no empirical measures of the causal properties of chess).
ML is the algorithms of statistics without any awareness, or use of, any scientific conditions on the data generating process.
It is entirely due to the experimental conditions which are a causal semantics on the data, not given in the data or in the algorithm -- something the experimenter or scientist will be aware of, but nothing the computer scientist will even have access to.
Regression is explanatory if the data set is causal, has been causally controlled, the data represents measures of causal properties, these measures are reliable in the experimental conditions, the variables under question each have causal relationship, and so on. Conditions entirely absent in the data and in the algorithm, and in anything to do with ML.
In a large number majority of cases where ML is applied, the data might as well be a teen survey in cosmo magazine and the line drawn an instrumental bit of pseudoscience. This is why the field is not part of scientific statistics -- because it aims to address "data as number" not "data as casual measure". The computer scientist thinks that ML can be applied to mathematics, or games like chess which is a nonsense scientifically (since there are no empirical measures of the causal properties of chess).
ML is the algorithms of statistics without any awareness, or use of, any scientific conditions on the data generating process.