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Review: The Book of Why

148 点作者 pizzicato大约 4 年前

13 条评论

michelpp大约 4 年前
Brady Neal has a great video course on the subject with slides and readings including Pearl and others:<p><a href="https:&#x2F;&#x2F;www.bradyneal.com&#x2F;causal-inference-course" rel="nofollow">https:&#x2F;&#x2F;www.bradyneal.com&#x2F;causal-inference-course</a><p>EDIT: I should add Brady also publishes his course textbook online, and it&#x27;s less Pearl-centric than The Book of Why but still covers the complete subject and then some:<p><a href="https:&#x2F;&#x2F;www.bradyneal.com&#x2F;causal-inference-course#course-textbook" rel="nofollow">https:&#x2F;&#x2F;www.bradyneal.com&#x2F;causal-inference-course#course-tex...</a>
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haberman大约 4 年前
&gt; The catch is that, whether explicitly or implicitly, you must make assumptions in the first place about the directions of causality among the variables.<p>That right there is the headline to me.<p>Compare this with the blurb in the dust jacket of the book:<p>&gt; &quot;Correlation is not causation.&quot; This mantra, espoused by scientists for more than a century, led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, led by AI researcher Judea Pearl and his colleagues, has cut through years of confusion about the nature of knowledge and established the study of causality at the center of scientific inquiry.<p>This all but claims that these new causal tools have found a solution to the problem of &quot;correlation is not causation.&quot; But they have done no such thing: there is no new technique offered here for establishing causation in any better or easier way than the RCT of yore.<p>If you get rid of the warning &quot;correlation is not causation&quot; and focus everyone&#x27;s attention on all the exciting inferences you can make when you <i>assume</i> causation, I&#x27;m worried that the end result is a lot of bad science.
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Anon84大约 4 年前
You might enjoy my blog series on Causality where I work through Pearls &#x27;Causal Inference in Statistics: A Primer&#x27; using Python:<p><a href="https:&#x2F;&#x2F;github.com&#x2F;DataForScience&#x2F;Causality" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;DataForScience&#x2F;Causality</a><p>&lt;&#x2F;ShamelessSelfPromotion&gt;
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breck大约 4 年前
I like the &quot;Ladder of causation&quot;:<p><pre><code> Rung 1: Associations, observational data (seeing) Rung 2: Intervention (doing) Rung 3: Counterfactuals (imagining) </code></pre> I often go in reverse order—let&#x27;s figure out the cheapest clever ways to prove ship will sink (imagining). Then if it seems like it might float let&#x27;s build it and throw it on the pond (doing). Then if it seems to float let&#x27;s hop on board and see what happens.
bachmeier大约 4 年前
&gt; This book dwells on the history of statistics a lot, and statisticians, as the authors would have you believe, are zealots who have conspired to keep causal thinking out of their field right from the start. That is, until Pearl instigated the &quot;Causal Revolution&quot;, as he dubs it, the latest and greatest gift to modern science. I have no dog in this fight, but Pearl (whom I assume is the source of most of these opinions put to paper by Mackenzie) often comes across as wildly biased and grandiose. For what it&#x27;s worth, I doubt that statisticians as a whole are anywhere as malicious or ignorant as they&#x27;re portrayed in this book.<p>This is correct AFAICT (I&#x27;m not a statistician even though I read a lot of the statistics literature). The strange thing is that I&#x27;ve never seen any obvious benefits to his comments of this nature. In the most generous possible reading, they are a distraction, with a less generous reading being that you can&#x27;t trust his interpretation of anything.
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kgwgk大约 4 年前
Another good book on the subject. Freely available.<p>Causal Inference: What If<p>Miguel A. Hernan and James M. Robins<p><a href="https:&#x2F;&#x2F;www.hsph.harvard.edu&#x2F;miguel-hernan&#x2F;causal-inference-book&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.hsph.harvard.edu&#x2F;miguel-hernan&#x2F;causal-inference-...</a><p><a href="https:&#x2F;&#x2F;cdn1.sph.harvard.edu&#x2F;wp-content&#x2F;uploads&#x2F;sites&#x2F;1268&#x2F;2021&#x2F;01&#x2F;ciwhatif_hernanrobins_31jan21.pdf" rel="nofollow">https:&#x2F;&#x2F;cdn1.sph.harvard.edu&#x2F;wp-content&#x2F;uploads&#x2F;sites&#x2F;1268&#x2F;2...</a>
pavlov大约 4 年前
<i>&gt; &quot;This book dwells on the history of statistics a lot, and statisticians, as the authors would have you believe, are zealots who have conspired to keep causal thinking out of their field right from the start.&quot;</i><p>I&#x27;ve read the book and have no dog in the fight. IMO this is an uncharitable interpretation of Pearl&#x27;s position. The authors of the book present the work of many past statisticians on both sides of the causal debate. A few influential people are indeed rendered as almost caricatures, but clearly that doesn&#x27;t represent the entire field when the authors also dive deeply into the work of other statisticians who explored causality.
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kenjackson大约 4 年前
I feel like I’ve tried to read several writings on this topic, mostly by Pearl. I feel like I’m good during the intro and motivation, but once it gets to the meat I’m completely lost. I feel like this is an area that would provide rich value if I could ever understand it.
albertTJames大约 4 年前
As asked by the author, here is an example of application in medicine: <a href="https:&#x2F;&#x2F;www.nature.com&#x2F;articles&#x2F;s41467-020-17419-7" rel="nofollow">https:&#x2F;&#x2F;www.nature.com&#x2F;articles&#x2F;s41467-020-17419-7</a>
ta988大约 4 年前
I highly recommend you to start with that one if you are new to causality. It is much more approachable than his other books.
rkabra大约 4 年前
I did a data scientist-focused review on the same book if that&#x27;s useful: <a href="https:&#x2F;&#x2F;medium.com&#x2F;@rishabhkabra&#x2F;book-review-the-book-of-why-by-judea-pearl-dana-mackenzie-44a87f71ad45" rel="nofollow">https:&#x2F;&#x2F;medium.com&#x2F;@rishabhkabra&#x2F;book-review-the-book-of-why...</a>
marttt大约 4 年前
Judea Pearl&#x27;s home page for the book might also be worth posting here: <a href="http:&#x2F;&#x2F;bayes.cs.ucla.edu&#x2F;WHY&#x2F;" rel="nofollow">http:&#x2F;&#x2F;bayes.cs.ucla.edu&#x2F;WHY&#x2F;</a>
neatze大约 4 年前
Does Judea Pearl other books overlap with The Book of Why ?
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