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smithsonionian t1_iywa7g5 wrote

Good article.

For further reading, “The Book of Why” by Judea Pearl.

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wavegeekman t1_iyz60ww wrote

Also "Causality", also by Pearl. More technical.

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YoungXanto t1_iyzy0jb wrote

There are two technical books he's published. One is Causality (2008) which is very technical and requires a fair amount of math background to understand and work through. He also has "Causal Inference in Statisrics: A Primer" which presents the core concepts with significantly less math pre-requisits

His do calculus is interesting, and he's highly influential in the machine learning literature, but he has a fair amount of detractors.

I personally like the concept that he presents in which we can reverse causality by re-ordering our equations. It points to the epistemological limits of our ability to understand causation in a way that Hume elucidated with his billiard ball examples a couple hundred years ago.

That said, Pearl is a bit arrogant for my taste, coming across as if he's the sole inventor of concepts that have existed for hundreds of years. His framework is a good one, but it is far from the only one.

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iiioiia t1_iz6m52s wrote

> His do calculus is interesting, and he's highly influential in the machine learning literature, but he has a fair amount of detractors.

This is a completely uninformed question but I am curious: are there any ML libraries you know of that specifically address causality (like, chains of causality, not simply direct correlation)?

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YoungXanto t1_iz6vnu0 wrote

I can't think of any off the top of my head, but I'm sure there is a plethora out there depending on what you want to do. A good starting point for Google is "DAG" (directed acyclic graph) which are basically the basis of Pearl's framework (and wildly useful in many other contexts)

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