It might be the worst read book I can think of. I am not even sure why, it just so painful to get through with the way the voice actor speaks.

Hopefully it gets a new rendition at some point in the future.

Michael Nielson has a nice post (circa 2012) on the topic at http://www.michaelnielsen.org/ddi/if-correlation-doesnt-impl... with comments at
http://www.michaelnielsen.org/ddi/guest-post-judea-pearl-on-....

Maybe something very simple:
A->B<-C similar to the syntax described for causal diagrams in Judea Pearl’s “The Book of Why”

On a related note, I have been reading Judea Pearl's new book, "The book of Why" (truly wonderful book incidentally). I think that stories must, in some way, help us learn counterfactual reasoning. That is, help us create alternate versions of reality to conduct "what if" experiments in our mind.

I first learned of Judea Pearl by stumbling across the transcript of a talk he gave while I was researching DAGs: http://singapore.cs.ucla.edu/LECTURE/lecture_sec1.htm . The way he grounded his talk in the history of thought hooked me, and the talk serves as a good general overview for those deciding if they want to pick up the book.

Deep learning models are effectively pattern matching machines that cannot separate causality from correlation. As we throw more compute and $$$ at deep learning models we will experience diminishing returns in performance because of this.

For us to achieve AGI[0], the holy grail of AI, we will need to develop algorithms that can recognize causality somehow. Judea Pearl’s “The Book of Why”[1] does a great job articulating why this is important. Deep learning is a big leap forward and we’re only beginning to see its impacts, but it’s not sufficient to achieve AI’s most ambitious goals.

[0] https://en.m.wikipedia.org/wiki/Artificial_general_intellige...

[1] https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/0465...

https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/0465...

Instead, he wrote about, what appears to be the same phenomena, the "cigarettes are bad deniers" of the 1900s.

2. I'll probably be off-point here, but maybe The Book of Why (Judea Pearl) could be interesting reading for you.

FloydHub has a great article about this topic on their blog:
https://blog.floydhub.com/best-deep-learning-books-updated-f....

But Blinkist also provides summaries for books that have a lot of ideas and depth (e.g. 'The book of why' by Judea Pearl) which would not benefit from being shortened. In these cases, a summary or blog post can whet your appetite, but isn't a substitute for the book.

Dreaming in Code by Scott Rosenberg. This book is, so far, the closest I've come to finding a "spiritual successor" to The Soul of a New Machine by Kidder. If you liked The Soul of a New Machine, or if you like watching Halt and Catch Fire, you may well like Dreaming in Code.

Inspired by Marty Cagan. Really solid overview of the essentials of product management.

The Book of Why: The New Science of Cause and Effect by Judea Pearl. Judea Pearl is, of course, a giant in the worlds of statistics and AI, and this book distills his work on "causal inference" and lays it all out in a pretty accessible manner. Not a textbook per-se, but not completely non-technical either. Read this if you're interested in how statistical analysis can be used to truly establish cause/effect relationships.

Capitalism: The Unknown Ideal by Ayn Rand. Do you think you hate Capitalism? Do you not understand why so many people love Capitalism? Have you based your opinion of Ayn Rand on second-hand commentary instead of actually reading her works? Then read this book.

http://apps.urban.org/features/school-funding-do-poor-kids-g...

If race is a mediator for income levels and in turn funding level shouldn't the researchers here include the difference in funding levels between income levels into their model to determine the true performance per spent dollar?

I might be wrong as I'm no expert in the field but I think these type of problems are what causal models try to address. I've really enjoyed "The book of why" by Judea Pearl on the topic. It got me interested in learning more about causality.

Basically Pearl argues that classical statistics completely ignored the concept of causality so far and introduces a complete framework to bring causal inference into statistical/data analysis. The framework is based on graphs and asks for causal hypotheses (like econometricians would do with instrumental variables) and allows to compute/quantify causal effects.

Anyone working with data should probably read this book. The fact that Pearl brought in a professional math/science writer as co-author is a huge boost to the main ideas accessibility and make for a nice albeit deep summer read.

[0] http://www.stat.columbia.edu/~gelman/arm/
[1] https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/0465...

I think some of this is exacerbated by modern pillars of machine learning and data science. Competition sites like Kaggle are entirely based on maximizing test set accuracy, and so winning submissions these days are huge morasses of ensemble methods that are trained for days and weeks on GPUs, but in the end they are often only marginally better than some of the fairly basic standard approaches. And when companies like Google are building their bots for Go or Starcraft, they are using cutting edge techniques. When people see that and get inspired to get into data science, thats what they want to do, even the the majority of problems are more rooted in data quality, thoughtful understanding of the problem, and more rudimentary methods.

Its also the result of some of the rhetoric of important figures in the field. Yann LeCun has pushed back strongly in the past on criticisms of modern day machine learning's occasionally lack of concern with introspection and model understanding. Judea Pearl, a Turing award winner for his work in machine learning, devotes large portions of his pop-sci The Book of Why attacking the field of statistics on the whole, as well as engaging in multiple attacks on historical influencers in the field with such ferocity it borders on character assassination. He has even rebuffed modern critics, such as the very widely respected Andrew Gelman, by saying they are "lacking courage" by failing to accept his "revolutionary" causal inference methods over the traditional ones used in statistics.

The attitude is driven a lot by the people and institutions at the top, and as someone in the field, I unfortunately encounter this kind of thinking way too often.

I’ll add it to my reading list.

I thoroughly enjoyed his "The Book of Why", a lay introduction to this subject.

For those on a more mathematical bend, check out "Causality" by the same author or "Causal Inference for Statistics, Social, and Biomedical Sciences" by Imbens and Rubin

https://samharris.org/podcasts/164-cause-effect/

#164 - Cause & Effect - A Conversation with Judea Pearl

August 5, 2019

In this episode of the Making Sense podcast, Sam Harris speaks with Judea Pearl about his work on the mathematics of causality and artificial intelligence. They discuss how science has generally failed to understand causation, different levels of causal inference, counterfactuals, the foundations of knowledge, the nature of possibility, the illusion of free will, artificial intelligence, the nature of consciousness, and other topics.

Judea Pearl is a computer scientist and philosopher, known for his work in AI and the development of Bayesian networks, as well as his theory of causal and counterfactual inference. He is a professor of computer science and statistics and director of the Cognitive Systems Laboratory at UCLA. In 2011, he was awarded with the Turing Award, the highest distinction in computer science. He is the author of The Book of Why: The New Science of Cause and Effect (coauthored with Dana Mackenzie) among other titles.

Twitter: @yudapearl

http://bayes.cs.ucla.edu/WHY/

https://www.amazon.com/Book-Why-Science-Cause-Effect

The reason I didn't like the book is that I found it insufficiently rigorous to really engage with the "how" of doing causal inference, but excessively mathematical as a theoretical introduction to causality.

"Causality: A Primer" (also written by Pearl) is a very short book that I think does a good job of surfacing some of the same theoretical background while also explaining how to use Pearl's causality. If you exhaust that, I'd recommend moving to the full "Causality" book.

But otherwise I'd recommend actually looking into the counterfactual / potential outcomes view of causality. The set of questions it answers are about 80% overlapping (although both Pearl and POs have their own 20%), but I find the vocabulary a little more intuitive. Canonical books include Morgan and Winship "Counterfactuals and Causal Inference" or Imbens and Rubin "Causal Inference for Social Scientists".

As to the blog post, Pearl is correct that causality requires qualitative assumptions about design to justify assumptions required to do causal inference. In Pearl's work this is often motivated as qualitative knowledge informing the structure of the DAG before any estimation. But recent advances in causal discovery have actually rendered it possible to black box the structure of a DAG from data -- happy to provide citations if this is down the rabbit hole. By contrast, I agree with Gelman that Pearl is an irritating writer and that in "The Book of Why" he gives a sloppy intellectual history of causation.

Haven't finished yet, he gets into artificial intelligence in the next few chapters, but he seems to make the claim that our brains work by doing a number of computations involved in causal inference beyond just Bayesian inference, such as subconsciously constructing causal diagrams and using them for causal inference including asking questions about counterfactual scenarios.

Would be interested to hear if anyone else who has read this book can help elaborate on this some.

Crashed by Adam Tooze ; history of the financial crisis, goes into more detail than most of the others

The Future of Capitalism by Paul Collier; lots of ideas on how to improve our situation, most of them are good, UK focused

Bad Blood by John Carreyrou; if you've ever been in a startup you'll recognize bits of this story, but it quickly gets out of control in novel ways. Astonishing story.

What Money Can't Buy: The Moral Limits of Markets by Michel Sandel

The Attention Merchants by Tim Wu

Who we are and how we got here by David Reich: ancient DNA and human history

The Book of Why by Judea Pearl; liked it but I need to reread this one a few more times to comprehend completely or go to his textbooks

Empire of Cotton: Sven Birckets; a history of the first global technology including how it made the UK & USA rich

The Away Game: The Epic Search for Soccer's Next Superstar by Sebastian Abbot

Unlike his previous books this is intended for general audiences rather than practitioners. It offers a good overview of Causal Inference, as well as a personal take on why there is such a split between his graphical approach and others such as SEM.

Overall, Pearl is unabashedly optimistic that statistics is finally on the verge of a "causal revolution", and this book tries to describe what that means. I'd recommend it highly, either as standalone or as background to accompany his more technical works.

This would be very helpful indeed.

I think a lot of the issue comes down to how idiosyncratic Pearl's work is. It will have to accumulate quite a lot of victories before enough people will bother with it.

Until then I suspect Causality will remain something of a statistical Finnegan's Wake.

I'll probably read The Book of Why to try and get a better handle on motivation for the technical material.

1. Black Like Me - John Howard Griffin -
https://en.wikipedia.org/wiki/Black_Like_Me

2. More Matrix and Philosophy - William Irwin (ed) - https://www.amazon.com/More-Matrix-Philosophy-Revolutions-Re...

3. Godel, Escher, Bach: An Eternal Golden Braid - Douglas Hofstadter - https://en.wikipedia.org/wiki/G%C3%B6del,_Escher,_Bach

4. The New Jim Crow - Michelle Alexander - https://en.wikipedia.org/wiki/The_New_Jim_Crow

5. Capitalism: The Unknown Ideal - Ayn Rand - https://www.amazon.com/dp/0451147952/ref=sspa_dk_detail_4?ps...

6. The Fountainhead - Ayn Rand - https://en.wikipedia.org/wiki/The_Fountainhead

7. The Book of Why: The New Science of Cause and Effect - Judea Pearl - https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/0465...

8. The Education of Millionaires - Michael Ellsberg - https://www.amazon.com/Education-Millionaires-Everything-Col...

9. The Silent Corner, The Whispering Room, and The Crooked Staircase - Dean Koontz - http://www.deankoontz.com/book-series/jane-hawk

10. Godel's Proof - Ernest Nagel & James Newman - https://www.amazon.com/G%C3%B6dels-Proof-Ernest-Nagel/dp/081...

11. After Dark - Haruki Murakami - https://www.goodreads.com/book/show/17803.After_Dark

The methodology of Austrian economics seems completely unscientific, I believe a part of praxeology is disregarding empirical evidence(?). Reminds me of something like Ayn Rand's egoism. It just seems to me you can't persuasively argue a philosophical theory without empirical justification

I have a more favorable view towards Polyani's methodology, who largely draws on historical sources. The historical approach seems at least some what grounded compared to the pure theory used in much of economics

Economic theory and statistics can't answer questions like "How much of an effect can we expect if we were to raise minimum wage by one dollar an hour".

A randomized control trial is the gold standard, and the way forward seems to be more experiments like the RAND Health Insurance Experience and the Oregon Health Insurance Experiment. Even these results and their policy implications are subject to debate, so how could pure theory even get close?

However, randomized experiments in the social sciences often aren't feasible for cost or ethical reasons. So econometrics has developed tools to work on natural experiments, or even observational data, like Differences-in-Differences, Instrumental Variables, Regression Discontinuity designs.

Even a brief skim of methodological considerations in economics reveals how much uncertainty there is. I am only a fan of economics (only high school and college microeconomics) so I'm likely wrong

disclaimer: I haven't really dug in to deep learning, so I'll wager there may be great resources I'm completely unaware of.

[0] 'Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press, https://www.deeplearningbook.org/

I encourage everyone to read "the book of why" by Judea Pearl. It illustrates how "research" and "studies" were used to sew doubt about the dangers of smoking. That's where we're at with social media, BUT, we have new tools like causal inference that should decrease the length of time we deal with "no link found here" or "correlation is not causation" bs.

Reading Judea Pearl's The Book Of Why certainly sobered my outlook on AI.

* Principles of Neural Design by Peter Sterling

* What Science Offers the Humanities by Edward Slingerland

* Category Theory for Programmers by Bartosz Milewski

* How Do You Feel? by Bud Craig

Completed reads:

* The Book of Why by Judea Pearl

* Letters to My Palestinian Neighbor by Yossi Klein Halevi

* The Glass Bead Game by Herman Hesse

* The Next Revolution by Murray Bookchin

* Democratic Confederalism by Abdullah Ocalan

* A bunch of trashy pulp scifi