> ML is an associative statistical system of function optimisation

You can also separate cause from effect by using causal inference, under some assumptions.

> ML makes the assumption that data points are IID.

Common ML algorithms do, but it is done for practical reasons rather than a limitation in the mathematics.

> The whole purpose of science is to produce models which explain why data isnt IID.

And ML can greatly help in this, though it is not a silver bullet.

Actually, there might not be a good way to model or describe the difference between causal inference, correlation and causality.

Causality involves a deep understanding of a phenomenon in science.

For example, the standard model of physics is pretty good at describing the real world in a good enough manner because we understand a lot of it. The difference with correlation and causality, in my view, is human, scientific understanding of what things are. Formulas, data or drawing are not enough.

For example there might never be a way to prove natural selection, even if there is a lot of data available, but a lot of scientific consensus is enough to describe causality.

When you say it's the opposite of science, you mean ML is just made of black boxes that completely hide away knowledge that humans can interpret?

Science is derived from scio, which in latin means knowledge.

It's true that in a way, ML allows new things, but which are still obscuring real knowledge...

I'm still curious about analysis of trained networks.