> It is impossible to inference causality from raw data - that is data without any a priori causality models.
It's impossible in general to be certain of causality for humans too, all we see are correlations in data and we invent causal theories that explain those correlations. That's basically what machine learning does as well: compile sets of correlated values into a compressed representation (the neural net) which arguably qualifies as a "causal theory" from the algorithm's perspective.
I think what's missing is maybe one or two orders of magnitude more compression, which would basically be devising a better/more parsimonious theory. We've been getting progressively better at that over the years too, and combined with how hardware has been scaling, we're seeing exponential growth in effectiveness. This is why some are predicting artificial general intelligence by 2030-2035.
> It's impossible in general to be certain of causality for humans too, all we see are correlations in data and we invent causal theories that explain those correlations.
We do experiments - we actually do them a lot and it starts very early with infants experimenting how activating a particular muscle moves his hand etc. Later we do less of original experiments - but repeat those that we already know the results - by walking for example and getting where we planned to.
In science to get from correlations to causation we have 'randomized trials'. In everyday practice we use something called 'free will' in our minds as the source of the needed independence.
> It's impossible in general to be certain of causality for humans too, all we see are correlations in data and we invent causal theories that explain those correlations
It's impossible in general to be certain of causality for humans too, all we see are correlations in data and we invent causal theories that explain those correlations. That's basically what machine learning does as well: compile sets of correlated values into a compressed representation (the neural net) which arguably qualifies as a "causal theory" from the algorithm's perspective.
I think what's missing is maybe one or two orders of magnitude more compression, which would basically be devising a better/more parsimonious theory. We've been getting progressively better at that over the years too, and combined with how hardware has been scaling, we're seeing exponential growth in effectiveness. This is why some are predicting artificial general intelligence by 2030-2035.