Introduction to Judea Pearl's Do-Calculus · HackerPapers discussions

Introduction to Judea Pearl's Do-Calculus — mentioned by Natsu

Tue, 10 Oct 2023 18:59:42 GMT

There is something missing: regular stats don't differentiate between doing things and observing things and these two are not at all the same. If I have a digital thermometer and I observe it to show a high temperature, then I will note an association between that and feeling warm. But if I merely set the thermometer gauge to a high value artificially, it's not going to make me feel any warmer. This ambiguity is resolved by something called do calculus - https://arxiv.org/pdf/1305.5506.pdf

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Introduction to Judea Pearl's Do-Calculus — mentioned by Natsu

Tue, 10 Oct 2023 18:53:38 GMT

This modelling ambiguity is resolved by do calculus, which makes a clear distinction between intervention and observation: https://arxiv.org/pdf/1305.5506.pdf

Paper and discussion history. A mention is not an endorsement.

Introduction to Judea Pearl's Do-Calculus — mentioned by yummyfajitas

Thu, 15 Jan 2015 10:14:03 GMT

This is an interesting illustration of correlation vs causality. By "causality", I mean links which remain even after you change the composition of your inputs. See the do calculus, which is one of the better attempts to codify it: http://arxiv.org/pdf/1305.5506v1.pdf http://ftp.cs.ucla.edu/pub/stat_ser/r402.pdf The do calculus attempts to describe causality as the correlations which remain present when you change the underlying composition of the sample set. In a certain sense, that's equivalent to saying it's the correlations which are not caused by Simpson's paradox.…

Paper and discussion history. A mention is not an endorsement.