Do Data Miners realise Machine Learning cannot establish Causality?
I think you're a bit confused about the role machine learning plays in the order of things, and I find your impression of the ML community to be a bit bizarre.

To begin, ML experts are often not the people who use ML algorithms to solve science problems - those people are usually called "data scientists" these days, and in principle they are supposed to have some sort of background in science, statistics, and/or mathematics.  And yes, (good) data scientists spend a lot of time worrying about causal inference - they have to because there are big companies with lots of money riding on their predictions.  Contrary to your impression ML experts are usually aware of these issues too, but their job is to build and analyze modeling tools rather than actually apply them to real data.  Of course there are lots of ML and data science people which straddle the line between the two fields, but my point is that your question is a bit like asking why mathematicians don't worry about experimental error.

That said, many (most?) ML algorithms are more oriented towards classification problems which are not quite as vulnerable to your concerns.  You still have to worry about sticky statistical problems like overfitting and building good training / validation datasets, but the focus is more on understanding the structure of the data that you have than predicting the future.