Understanding Causality Is the Next Challenge for Machine Learning

Re:Understanding Causality Is the Next Challenge for Machine... by turing_complete on Thursday October 29, 2020 @01:00PM (#24932593)

I recommend "The Book of Why" by Judea Pearl as an introduction to the topic. It is targeted at a general (but educated) audience.

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Re:Understanding Causality Is the Next Challenge for Machine... by jasonwatkinspdx on Thursday October 29, 2020 @02:24PM (#24933632)

The Epilog to his book Causality is a reasonably short read and does a good job of introducing the concepts imo. It's basically the same content he used in his Turing award speech and similar presentations over the last decade or so: http://bayes.cs.ucla.edu/BOOK-2K/causality2-epilogue.pdf

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Re:Understanding Causality Is the Next Challenge for Machine... by epistasis on Thursday October 29, 2020 @01:28PM (#24932882)

For some shorter reads, I saw this recommendation for biology undergrads yesterday:

> I would really love all biology students to read Elliott Sober's "Apportioning Causal Responsibility", Susan Oyama's "Causal Democracy and Causal Contributions in Developmental Systems Theory", and Richard Lewontin's "The Analysis of Variance and the Analysis of Causes".

https://twitter.com/hanemaung/status/1321488068717719552?s=2...

These are available through research gate for those without library access.

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