What is Reddit's opinion of The Book of Why: The New Science of Cause and Effect?

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The Book of Why by Judea Pearl

"Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality--the study of cause and effect--on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.

That's not how causality works.

• if you were in control of data collection, you need to separately collect data for a control group and treatment group. The application of an intervention to the treatment group makes causal inferences basically trivial.
• if this is observational data that you are trying to do causal inference on post hoc, it's not impossible but way more complicated. Often involves approximating a "conditional average treatment effect" (CATE) by separately modelling individuals "matched" into a simulated control group vs a simulated treatment to estimate "counterfactual" phenomena.

Causal modelling is a whole rabbit hole. Check out this book for an intro: Judea Pearl - The Book of Why

It's not a matter of computing power necessarily. We miss a piece of the theoretical puzzle here.

Check out this book if you're interested in where the biggest innovations still have to be made in computer science:
https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X

It's great to see this. Deep learning models currently rule the world, but they are based entirely upon statistical correlation, and don't have representations of objects or classes and the causal relationships between them. In the meantime, the science of causal reasoning has been making serious progress in the past decade or two. They will need to be combined in order to create grounded models that can generalize.

For those of you who have not been following causal reasoning, I'd recommend the following books:

• J. Peters, D. Janzing, and B. Schölkopf. Elements of Causal Inference - Foundations and Learning Algorithms. MIT Press,Cambridge, MA, USA, 2017
• Pearl, J. Causality: Models, Reasoning, and Inference, 2nd. Cambridge University Press, New York, NY/ 2013

For a more popular and historical account, you can read:

• Pearl, J. The Book of Why

Not finished with it yet, but so far Judea Pearl’s the Book of Why is really good too. His research and philosophy is extremely unique IMO bec ause he is a computer scientist by training educated in Machine and deep learning, but a lot of his work has focused on understanding causality. The book discusses why causality is so important and the need for us to solve that problem before we can get computers to pass the Turing Test. IMO extremely relevant to I/Os attempting to blend theory with AI.

https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X

Warning: Long screed of Computer science/statistics/data science incoming!

It’s not just a lack of empathy. It’s origin lies in how their minds work, on more of an algorithmic level. Empathy requires a type of speculative imagination --- that fleeting moment of considering another’s feelings --- the moment where you count the cards that are your experiences with another person, and wildly surmise to the best of your ability the place where they are coming from.

(think DMB, “Dancing Nancies” or “Cry Freedom, Cry”)

When Bayesian statistics started taking off in the 60’s and 70’s, it shook the mathematics world. More recently, Pearl’s mathematics of cause and effect also give clues in the wild surmisation of empathy,- as to what characterizes modern Republicans.

I believe Republicans lack empathy because they are terrible at counting cards.

Take, for example, the way they are Propagandized. Why are they so easily Propagandizable? I believe it is because they have little inherent ability to incorporate evidence. We have, in 2018, a predicament of complexity in understanding.

A simple understanding is simply not possible. A veiled conspiracy and tangled web of hidden connections and deals have been made by Trump and his entourage. On reddit, etc, trolls who could be real, propagandized, human or robot, from a variety of countries contribute to the dialogue with unrevealed motives. There is not just one dialogue with ‘facts’, and another with ‘alternative facts’.. The interplay of the two is more than twice as complicated as a straightforward reality.

A proper understanding of the truth is simply not simple, but for those who are unable to count cards, it is simply not attainable. When the narrative diverges from the propaganda’ed line, a lie is created. That lie is hammered in with a Frequentist approach,- over and over the same lie.

To a Republican, believing the oft-repeated lie or giving attention to the narrative-supporting distraction is both substantially easier and less cognitively dissonant than to try to incorporate contrary evidence into their mental model. (I am not even sure that many could do it even if they wanted to). Our best hope for these people is to somehow stop the frequentist propaganda supporting their beliefs. (“lock her up”, anyone?)

I could go on much longer, but for those who have a sufficient mathematical background, this sketches out the basis of where I think the Republicans are coming from in terms of a mental model. Whether it is a difference in wiring (algorithm-based), or data structure (mental model), I do not know. But why not either/or both? (there is a feedback loop there, for sure)

For anyone who got some understanding from this post, I recommend Judea Pearl’s “Book of Why - The New Science of Causation”, there are more points I could make in relation to it, but not many could follow without first reading it (particularly the 2nd and 3rd rung of causation). I hear it’s all the rage this year among data scientists at Google, but is written for laymen.

Different people have different talents. Republicans suck at statistics. It’s their defining characteristic. They’re frequentists all day long.

https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X

thanks for the gold, kind stranger. I’m glad you appreciated it!

• The Book of Why: The New Science of Cause and Effect
• Algorithm Design
• An Introduction to Statistical Learning

Three obvious recommendations would be
1) Kevin Murphy's book series on probabilistic ML
2) Goodfellow/Bengio/Courville's Deep Learning Book
3) Sutton/Barto's Reinforcement Learning

Information Theory, Inference, and Learning Algorithms.

But honestly, there are just so many different paths you could pursue... you could dive into causality (Pearl's Book of Why, Overview article by Schölkopf, technical book by Peters/Janzing/Schölkopf), a more control theory-oriented perspective on RL (Meyn's 2022 book, Bertsekas' Lessons from AlphaZero for Optimal, Model Predictive, and Adaptive Control and basically any of his other books), convex optimization, ...

The Book of Why
https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X

Statistical Rethinking
https://www.amazon.com/Statistical-Rethinking-Bayesian-Examples-Chapman/dp/1482253445

https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X

The problem with most statistics in academia is that it's focused on scientific research, which means frequentialist and everything revolving around p-value. But that's just one side of statistics, not necessarily the side of statistics companies are interested in.

I would start with the Book of Why by Judea Pearl. Judea is cutting edge and looking ahead of where statistics is going, causal inference:

https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X

And The Art of Statistics is a great general primer on statistics:
https://www.amazon.com/Art-Statistics-Learning-Pelican-Books/dp/0241398630

In Judea's footsteps comes Richard McElreath. He's teaching Bayesian statistics and his book paired with his youtube channel is a great primer on this.

https://www.amazon.com/Statistical-Rethinking-Bayesian-Examples-Chapman/dp/1482253445 https://www.youtube.com/channel/UCNJK6_DZvcMqNSzQdEkzvzA

But perhaps by now you're too far into the reeds of Bayesian statistics. Though I believe this is what employers want, employers might not yet know that this is what they want and instead they just want the type of statistics that people know right when they graduate:

At which point you'd want the Springer trifecta:
The three basics:

Introduction to Statistics and Data Analysis
https://www.amazon.com/Introduction-Statistics-Data-Analysis-Applications-ebook/dp/B01N177FKN
Now this is taking it from the very base of probability. So that means only two thirds in you get to deal with regression. Might not be worth it unless you feel you don't have secure you got a solid foundation (which I imagine a lot of CS people might feel).

An Introduction to Statistical Learning
https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1071614177
I consider this the bible of Data Science. The 2nd edition just arrived last week. It teaches machine learning for statisticians, which sounds weird because all machine learning is statistics but a lot of machine learning books get you bogged down in programming individual nodes without the context of making it applicable for a business.

The Elements of Statistical Learning
https://www.amazon.com/Elements-Statistical-Learning-Prediction-Statistics/dp/0387848576
Title is misleading, this is anything but elementary. I must admit I'm only a third into this book. A lot of it is still more than I can chew but it's already proven useful as a reference guide.

Bonus:
Data Science for Economics and Finance
https://www.amazon.com/Data-Science-Economics-Finance-Methodologies/dp/3030668908
Also brand new. Feels weird to read about covid in a Springer book. This isn't really a tutorial book. It's more of a series of lectures and case studies in fintech. It's great if you want to be able to keep with the data-joneses as it explores a lot of ideas still in their infancy.

Final note: Some of these books are free ebooks. If you don't value physical copies like I do, then make sure you google their pdfs before buying them.

Absolutely. /u/vwcanter has a very nice answer. I want to add some different perspectives.

Belief Propagation is an algorithm introduced by computer scientist Judea Pearl. Like Pearl explains in The Book of Why, your phone exploits this algorithm to determine who's trying to communicate with who regardless of noisy signals. It assigns probabilities to different beliefs and tests them against each other relative to available evidence. In other words, it's an inferential engine that can compensate for missing information by having different beliefs "fight it out".

Let's say that there are a bunch of different answers that could potentially explain what you're hearing right now. You hear "I have to get off this -oat". What did you hear? Was it goat, boat, float, moat, coat, rote, or vote? The rest of the sentence implies that "-oat" is something you can get on and off of. We can dismiss rote, vote, and moat as nonsensical in this context. Was it goat, boat, or coat? That also depends on context. Was it said by someone on a farm? Then we can rule out "boat". Was it said by someone in a dressing room? Then we can rule out "boat" and "goat". Was it said out at sea? Then we can rule out "goat".

By pitting alternative explanations against each other, we're basically enacting a sort of Darwinian "survival of the fittest". When there's no evidence to "feed" an explanation, it dies off.

There is a lot of evidence indicating that our brains exploit the Belief Propagation algorithm to facilitate causal inference.

Right now as you're reading this, you're predicting what words are likely to follow. You have a generative model that spits out hypotheses and tests them against available information. You're constantly predicting what's going to happen next. Which is why ice-cream cones make for great hats. Notice what happened as you read that last non-sequitur of a sentence. You didn't expect it, so you became surprised. It's impossible to become surprised without already having expectations: if they're not already present, they can't be violated.

Philosopher Andy Clark has written about the predictive mind in Surfing Uncertainty. As has Jacob Hohwy, in The Predictive Mind.

Prediction is an integral part of the musical experience. Try listening to this clip two times. The first time, you hear a sentence. Then after listening to one part of it several times, it becomes music. And if you listen to the clip again, the woman speaking seems to suddenly break of in song mid-sentence. It's a very fun illusion, and it tells us that familiarity with patterns can alter our experience of them. Once you find the pattern, it's music to your ears.

References:

Friston, K. J., Parr, T., & de Vries, B. (2017). The graphical brain: Belief propagation and active inference. Network Neuroscience, 1(4), 381–414. https://doi.org/10.1162/netn_a_00018

Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204. https://doi.org/10.1017/s0140525x12000477

McEliece, R. J., MacKay, D. J. C., & Jung-Fu Cheng. (1998). Turbo decoding as an instance of Pearl’s “belief propagation” algorithm. IEEE Journal on Selected Areas in Communications, 16(2), 140–152. https://doi.org/10.1109/49.661103

‌Pearl, Judea (1982). Reverend Bayes on inference engines: A distributed hierarchical approach. Proceedings of the Second National Conference on Artificial Intelligence. AAAI-82: Pittsburgh, PA. Menlo Park, California: AAAI Press. pp. 133–136. Link to paper.

I just started reading this book, The Book of Why by Judea Pearl.

> Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.

I first heard about him from a Sam Harris interview. There are a few talks by Pearl on youtube also.

Ultimately the answer is "We don't know. Therefore God exists." LOL. Sorry. Bad joke.

There does seem to be a difference between the quantum level and our macro or "emergent" level where we live and think. At least that is what we experience.

> I meant a reason as in if it happened in any other way it potentially wouldn't be better than it is right now.

We seem to be constrained by the arrow of time if we are wanting to change the past. Could past events have been different than they were? I don't think so. I think they are what they are (they were what they were). We cannot change them from our position today and we could not have changed them from our position in the past.

Apologies for the long answer, but this is obviously a complicated topic and I want to do it justice. I do need to point out that I am not a stream-winner, nor have I had any first-hand experience of the truth of rebirth, so I am certainly not an authority on this subject. I can relate to your comment, as I'm a scientist myself and an avid reader of religious texts who has only very recently been convinced about the Buddhist concept of karma, so hopefully this will hit home for you if you read it. Still, I'd highly recommend reading the arguments put forth by others on this topic, particularly Thanissaro Bhikkhu, over what I have to say. He's obviously a much more advanced meditator than myself, one of the foremost Buddhist scholars alive, and also a better writer than myself. A good place to start is his introduction to MN101, the sutta where the Buddha is discussing karma with his Jain contemporaries, as this makes clear the way the Buddha conceived of karma. He has also written a short essay on karma, which would be a good quick read for more background. Faith in Awakening is a longer essay which deals again with this topic, and more specifically with the nature of faith in Buddhism which is very relevant to this discussion. Best of all, though, would be to read his short book (available for free online) called The Truth of Rebirth, as this dives into every aspect of this debate and is very eloquent.

Still, none of these sources will provide scientific evidence of rebirth and karma, nor will I. The fact is it doesn't exist, and science as we know it is nowhere near even being able to articulate issues like these. There's no evidence against it, and no evidence for it. We still have no real idea how consciousness works, even on the level that we all experience as real, let alone consciousness as it might transfer between births. Science currently has no way of testing if free will exists, on any level, nor does it have any way of knowing what happened before the big bang. For that matter, science has only developed a mathematics and language for discussing causality - on any level - in the past two decades (see The Book of Why for more on this). Nor is there any way, as of now, to test the real efficacy of Buddhist meditation in alleviating suffering - it's something we can experience ourselves, and believe from the accounts of thousands of others, but still, there's no way to measure such a subjective thing. I'm a biologist, I know that science has done wonders for the world and we know a hell of a lot, but we simply aren't even close to understanding the basis of these kinds of questions, if they're even possible to answer this way. Still, you asked for any scientific or even logical answer, and there is definite logic to believing in karma.

The idea of karma, from a Buddhist perspective, just doesn't have to be accepted by science to be useful. What is important is that it is a skillful perspective - it provides a useful framework for understanding the universe that helps us generate conviction in the efficacy of our actions, compassion and empathetic joy for the suffering and success of others, and a sense of the importance of action over the importance of the "I" doing the action. That's why it's included in the noble eightfold path as the mundane level of right view; faith in and understanding karma is important because it assures us that practice works and illustrates how and why it will work.

The Buddhist conception of karma was different from the contemporary ideas of karma because of one major distinction: karma has both a past and a present quality. Our past actions and intentions are ripening in this moment as one factor of our present experience, but our current actions and intentions also shape our present experience as well. This is why we are able to practice; despite whatever conditions are coming to us from the past, bringing us pleasant, painful, or neutral feeling, we still have the capacity in the present to act with skillful intentions and change our perception of those feelings and experiences. Skillful action therefore doesn't only generate good karma that we'll reap in the future; it also changes the way we experience ripening of karma in the present - which ultimately allows us to practice, train the mind, and liberate it from suffering, such that the continued ripening of our past karma can no longer cause us suffering.

There are a few important things to note about this that I hope will answer some of your questions. The first is that the notion of rebirth is not strictly necessary for an understanding of karma. There's good reason to include rebirth - it lends a greater sense of urgency to the practice if our suffering has been immeasurably long and will continue to be, and it clears up some of the confusion caused by karmically negative actions seemingly having positive external consequences by providing enough opportunity for the ripening of all karma in the future. It's also important to note that the Buddha first attained an understanding of rebirth and karma - on both the large scale of multiple lifetimes and the small scale of dependent origination from mind to mind - that led to the ultimate insights into the four noble truths and the end to suffering. Given that he taught only the fraction he deemed necessary for the end of suffering out of all that he learned in the course of his enlightenment, it makes sense that it's important.

Still, though, karma works on large and incomprehensibly complex scales, but it also works on small scales. If you don't want to believe in the rebirth aspect, it's impossible to deny the most visible and obvious examples of cause and effect. If you're angry and upset and you kick something too hard, you will experience unpleasant feeling. That's bad karma ripening immediately. How you react to that pain in the present can change how or if you suffer from it, and that's the present aspect of karma, and the basis of the entire path. On a longer scale, reacting angrily in this way might strengthen your habit of getting angry, leading to more anger in the future. That's more ripening of bad karma in the future. Maybe you kicked a table and something broke. Someone will get mad at you for it, or you won't like paying for it, or whatever, and that's more ripening of bad karma. Understanding that this works on small and large scales is important, because it shows how important your actions are. They have immediately consequences, and long-term consequences - consequences which interact with your other karma and the world at large in such varied and complex ways that it's impossible for us to trace the exact path.

Hopefully you can agree with at least this partial example of karma. Things we do have consequences, and things we do with bad intentions tend to have bad consequences. Understanding this is important because it encourages right action, right speech, and right effort on the path so that our actions won't come back to bite us. Having the sense that this is true of every single bad intention and action, with results being as small as a stubbed toe and as big as lifetimes in the hell realms, adds a sense of weight to the idea. It also allows us to have a more skillful perspective in the face of hardship. With the idea of karma in mind, we can't blame anyone else for our unpleasant situation; our past actions have caused it all. Working to change our external environment can have positive effects only if we do it with positive intentions, rather than out of greed or aversion or delusion. Similarly, any goodness that comes in life is from our own skillful action; we should do more of that to get more in the future. The emphasis here is also important; skillful actions bring about pleasant results - it doesn't matter how much bad or good you've done, skillful actions will bring good results in the future. That allows us to focus on our actions, rather than our being, as we can only change and control our present intentions and actions. Without the principle of karma, we are left with feeling that everything is predetermined so it doesn't matter what we do, or our actions don't have consequences so we can do what we like, or even if our actions have consequences and we can make choices, we won't experience the results after we die so there's no need to worry overly much. None of these are skillful, and none of them lead to release from suffering, in this life or any other.

As for the harm you see the idea of karma doing, it's an unfortunate truth of humans that any idea can be corrupted. Any non-arahant human, even with a perfect understanding of the reality of karma, can be overcome by greed, aversion or delusion and attack others with their ideas. And with the widespread nature of Buddhism, there's naturally many people with misunderstandings, and disagreements even between entire schools and branches of the tradition. Just because some people are misguided or unskillful doesn't mean an entire concept is harmful. The fact is, the idea of people "deserving" their lot in life based on karma doesn't really make any sense. Does the moon "deserve" to orbit the Earth because of gravity? No, the world just works that way. Karma is the same; some people experience unpleasant things because of past unskillful intention and action. That's just how it works, and it works the same for everyone whether we like it or not. Now, that doesn't mean we should be mean to people who have a hard lot in life. In fact, it means we have the opportunity to really help them, because changing their present actions and intentions into more skillful ones will improve both their future karmic fruits and their current experience of the present. We can have compassion for those who are suffering, or for ourselves when we suffer, because we can understand the impersonal law behind it all. This should guide us to greater kindness, greater effort in our practice, and a greater sense of perspective on life.

I know this post is already too long, but the last thing I'll say is that the Buddha taught that things like the origin of the universe or the exact workings of karma are things we shouldn't think about. We don't derive benefit from considering them, we just waste our time and drive ourselves mad. Yes, if we are scientists and we can contribute to the world by actually conducting meaningful experiments to test it, that has mundane value as a profession. But for the purposes of the Buddhist path, for the elimination of suffering, it's just a distraction. Having faith in karma will help one make skillful choices and decide to follow the path. Asking incessant questions about why we are suffering in this way or exactly how it works is foolish, as the Buddha demonstrated with an analogy. If we have just been shot by an arrow, we don't say "Tell me who shot this arrow, what clan he was from, who he works for, why he shot the arrow, what the arrow is made of, where he got the parts, how he fashioned the shaft, and where he found the tree to get the wood, before you dare treat me!" We'd die before we ever found the answers. What we know is that we've been shot by an arrow, and that the doctor is telling us he has taken out arrows before and knows how it's done. Let's not waste our lives away asking questions when we can just take it out.