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<blockquote data-quote="Cadence" data-source="post: 9400495" data-attributes="member: 6701124">Finally got back to this tangent...&quot;Causal Inference&quot; is currently one of the hot topics in statistics and related fields.&nbsp; The big focus is examining what can be done if you don't have an experiment, but can make some other assumptions. Like most things in statistics it will probably be horribly misused in some cases.&nbsp;&nbsp; Anyway, these are four things to look at that I've added to my stack to skim.A 2022 interview/history overview with Judea Pearl is at <a href="https://ftp.cs.ucla.edu/pub/stat_ser/r523.pdf" target="_blank">https://ftp.cs.ucla.edu/pub/stat_ser/r523.pdf</a> .&nbsp; Pearl is a computer scientist and phiolosopher at UCLA.A chatty, recent, online, intro book on it is Causal Inference for the Brave and True (using Python and memes) at <a href="https://matheusfacure.github.io/python-causality-handbook/landing-page.html" target="_blank">Causal Inference for The Brave and True — Causal Inference for the Brave and True</a> . It still assumes you've had some intro stats with expected value notation, but has a chapter on the graphical models mentioned in the two above.&nbsp; It's intro notes 'Your parents have probably repeated to you that “association is not causation”, “association is not causation”. But actually, explaining why that is the case is a bit more involved. This is what this introduction to causal inference is all about. As for the rest of this book, it will be dedicated to figuring out how to make association be causation.'&nbsp;&nbsp; I can't vouch for it, but didn't find anything easily online torching it and saw several liking it.&nbsp; (I'm partially interested in it to see how they would teach it to students with the background it is aimed at).A 2010 introduction to Causal Inference is at:[URL unfurl=&quot;true&quot;]https://www.cmu.edu/dietrich/philosophy/docs/scheines/introtocausalinference.pdf[/URL]Scheines is a philosophy prof at Carnegie Mellon. His 2000 book with Spirtes and Glymour is widely cited.This is a 2015 review of some basics of Causal Inference.&nbsp; It appeared in the Handbook of Big Data.&nbsp;&nbsp; <a href="https://arxiv.org/pdf/1506.07669" target="_blank">https://arxiv.org/pdf/1506.07669</a>Maathuis is a stats prof at ETH Zurich who specializes in Causal Inference and Nandy is a data scientist at Google.</blockquote>

[QUOTE="Cadence, post: 9400495, member: 6701124"] Finally got back to this tangent... "Causal Inference" is currently one of the hot topics in statistics and related fields. The big focus is examining what can be done if you don't have an experiment, but can make some other assumptions. Like most things in statistics it will probably be horribly misused in some cases. Anyway, these are four things to look at that I've added to my stack to skim. A 2022 interview/history overview with Judea Pearl is at [URL]https://ftp.cs.ucla.edu/pub/stat_ser/r523.pdf[/URL] . Pearl is a computer scientist and phiolosopher at UCLA. A chatty, recent, online, intro book on it is Causal Inference for the Brave and True (using Python and memes) at [URL='https://matheusfacure.github.io/python-causality-handbook/landing-page.html']Causal Inference for The Brave and True — Causal Inference for the Brave and True[/URL] . It still assumes you've had some intro stats with expected value notation, but has a chapter on the graphical models mentioned in the two above. It's intro notes 'Your parents have probably repeated to you that “association is not causation”, “association is not causation”. But actually, explaining why that is the case is a bit more involved. This is what this introduction to causal inference is all about. As for the rest of this book, it will be dedicated to figuring out how to make association be causation.' I can't vouch for it, but didn't find anything easily online torching it and saw several liking it. (I'm partially interested in it to see how they would teach it to students with the background it is aimed at). A 2010 introduction to Causal Inference is at: [URL unfurl="true"]https://www.cmu.edu/dietrich/philosophy/docs/scheines/introtocausalinference.pdf[/URL] Scheines is a philosophy prof at Carnegie Mellon. His 2000 book with Spirtes and Glymour is widely cited. This is a 2015 review of some basics of Causal Inference. It appeared in the Handbook of Big Data. [URL]https://arxiv.org/pdf/1506.07669[/URL] Maathuis is a stats prof at ETH Zurich who specializes in Causal Inference and Nandy is a data scientist at Google. [/QUOTE]

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