[jawbone3]

Astronomy strives to be a science, so ultimately it needs to tune causal models with data. NNs will by their nature never be causal (their entire point is that they can approximate everything), so they will be used like here to find candidates to investigate with real models.

[nl]

The article says they use the neural net to find lenses which match the model the astronomers develop:

To train the neural networks in what to look for, the researchers showed them about half a million simulated images of gravitational lenses for about a day.

I'm not sure what you mean by a "real models" in any case.

Things like NN based generative models combined with model selection certainly can build models which discover real-world behavior. There's a long history of this in the disease epidemiology field. In these cases it isn't usually neural network based, but that is mostly about the most appropriate learning algorithm for the data available.

[xaedes]

"NNs will by their nature never be causal (their entire point is that they can approximate everything)".

A NN can sureley result in a traditional causal model.

For example: Build a NN that has the same computational structure as some simple physics law. By giving it training data it then figures out necessary constants. That may converge to the traditional model, which is mirrowed in the network architecture anyway.

So I really can't support saying NN will by their nature NEVER be causal.

[jawbone3]

I'm not saying a NN doesn't reproduce analytical expressions, rather that "take everything and mix it together a bunch of times using this recipe" is not the form that the laws of nature has.

I'm not sure what you think explicit construction proves, it is clearly a case of taking preexisting knowledge and expressing it in what is doubthlessly a more akward form.

[jacobush]

Especially since humans arguably uses wetware neural nets and don't we like to think we are coming up with causal models?

[sanxiyn]

Well, "neural nets that come up with causal models" is a research problem and we don't know how to do so.