If you're wanting to read more about causal inference, I liked this flowchart on "Which causal inference book you should read"<p><a href="https://www.bradyneal.com/which-causal-inference-book" rel="nofollow">https://www.bradyneal.com/which-causal-inference-book</a>
I suspect we'll see causal techniques start merging with more traditional AI/ML tools over the coming years.<p>Causal forests are an example that extends random forests, but I imagine a lot of the value in current pipelines would be to use causality as regulariser. This could be a parameter that controls the weight of established causal links, or it could be as a scaffold; e.g. a first 'causal pass' is used to establish constraints (monotonicity, conditional variable selection, reject changes that result in predictions inconsistent with the initial causal model when there is a strong causal model etc).<p>RL is likely more promising. If agents could be made to search for causality in an environment these relationships could be made much harder to unlearn which would then enable more efficient exploration & incremental learning. Framed this way causality guides attention, limits the search space and locks in learning.<p>I've got some quarantine reading/experiments to try! :)
Elias Bareinboim [1] mentions "we are beta-testing a tool called ‘Fusion’, which offers an easy-to-use way of doing causal inference from 1st principles" [2]. Tantalizing, but I've not yet seen anything else about 'Fusion'.<p>[1] <a href="https://causalai.net/" rel="nofollow">https://causalai.net/</a>
[2] <a href="https://twitter.com/eliasbareinboim/status/1191609450462883841" rel="nofollow">https://twitter.com/eliasbareinboim/status/11916094504628838...</a>
I have been working on a PhD about causal graphs for the last 6 years<p>I wonder what the career perspectives that brings? I want to do no statistics, only programming