
“The Book of Why” by Pearl and Mackenzie - nkurz
https://andrewgelman.com/2019/01/08/book-pearl-mackenzie/
======
nkurz
My comment on the blog was (at least initially) eaten by the spam filter, so I
reproduce it here.

\---

JP: There is no way to answer causal questions without snapping out of
statistical vocabulary.

AG: We disagree. That’s fine. Science is full of disagreements, and there’s
lots of room for progress using different methods.

I'm only an amateur, but from the outside it sure doesn't feel "fine" for the
two of you to disagree on what seems like such a fundamental issue. Instead,
this seems like a case where two extremely smart individuals should be able to
reach an common understanding instead of accepting disagreement as a final
outcome.

JP: I have tried to demonstrate it to you in the past several years, but was
not able to get you to solve ONE toy problem from beginning to end.

AG: For me and many others, one can indeed answer causal questions within
statistical vocabulary.

Pearl obviously disagrees that standard statistical vocabulary is sufficient
to answer all simple causal questions. You seem to think he's wrong. I think
you'd be doing a great service to encourage him to formulate such a "toy"
question that he thinks is unanswerable without resorting to the do-calculus,
which you then try to answer to the audiences' satisfaction using more
standard techniques. Maybe the two of you turn out to be in agreement but
using different terminology, maybe you are right that his tools are optional,
or maybe he's right that they are essential. Any of these outcomes would feel
much more satisfactory and productive than agreement to disagree. Please
consider offering him a platform with which to make his case.

~~~
bachmeier
> it sure doesn't feel "fine" for the two of you to disagree on what seems
> like such a fundamental issue. Instead, this seems like a case where two
> extremely smart individuals should be able to reach an common understanding
> instead of accepting disagreement as a final outcome.

Once you've reached the limits of what you can know (after examining all data,
worked through all arguments, etc.) this is pretty much the only possible
outcome. What works in one situation might not work in others. One person's
interpretation of our limited knowledge might very well appear implausible to
someone else.

I'll admit to being turned off by Pearl's insistence on working with "toy
problems". That might be fine for a philosophical discussion, but it's not of
much practical value. I want Pearl to write a few empirical papers attacking
important issues, then let's have a discussion about putting ideas into
practice.

~~~
nkurz
I have much more sympathy for Pearl. He's constructed "toy" problems that
yield different answers depending on the hidden causal structure chosen. He
then shows that unless one takes account of the causal structure, it's
impossible to correctly answer the problem. The logical conclusion of this is
that any technique that doesn't use the causal structure as an explicit
assumption is instead using it as an hidden implicit assumption. His hope is
that anyone who goes through his exercise will come to the same conclusion.
Starting with real-world problems is tricky, because one doesn't know the
correct answer in advance, thus it's much harder show the reliance on the
assumptions.

Consider a parallel with computer programming. A user complains that they fear
a program is giving them the wrong answer on a complex real world problem.
They report this, and get back the unhelpful answer "Works for me, will not
fix". Unable to shake the feeling that the answer is unreliable, they reduce
the problem down to a proof of concept that serves as a simple self-contained
test case. Two different inputs produce the same answer, but only one of them
can be right! But now they are unable to convince the maintainer to even look
at the test case, because now the maintainer says "I need to focus on the real
world, and don't want to waste my time on toy examples".

It's a discouraging position to find oneself in.

~~~
bachmeier
Oh, I'm completely on board with being explicit about any assumptions you're
making, and for thinking deeply about the causal relations, and for thinking
about the reliability of the analysis. I've been critical of economists for
not thinking things through. And I'm definitely on board with teaching using
toy problems.

The problem is that, as far as I can tell, Pearl doesn't go beyond making
points with toy problems. He hasn't done much empirical work (has he published
a single empirical paper?) or even read much of the empirical literature he's
criticizing as worthless. Ultimately, the question is whether policy and other
decisions will be better using a particular framework. The fact that Pearl
writes with the aggressive confidence of a Hacker News commenter does not mean
he's right.

~~~
harperlee
But from a mathematical point of view this insistence of focusing on empirical
work is a little bit disheartening isn’t it?

And frankly Judea Pearl is hardly a random internet tinfoil hat guy, nor is he
asking people to invest a massive effort checking his looong work (as we eg
often see with random NP-completeness “proofs”, or was discussed with the time
investment needed to check Mochizuki’s ABC proof - no, he just asks Gellman to
apply his own familiar techniques to a toy problem. It does not sound
unreasonable at all.

~~~
bachmeier
> But from a mathematical point of view this insistence of focusing on
> empirical work is a little bit disheartening isn’t it?

Why? The only thing that matters is the quality of the empirical work. You can
get caught up in philosophical debates about the best way to do research. If
it has no impact on empirical work, it's useless.

That's not to say Pearl's arguments are wrong or that his work is useless. The
problem is the incompleteness of his arguments. You can't arrogantly dismiss
empirical work just because it's imperfect. There's no reason a priori to
expect that Pearl's approach will lead to better decisions.

There are many self-proclaimed experts who can point out the flaws in
programming language designs, but that doesn't mean they can design a better
language, and it doesn't mean existing programming languages are useless.
Pearl's approach is not some kind of magic pixie dust that suddenly guarantees
your empirical work is more trustworthy. It's unfortunate that Pearl thinks it
is, and it prevents him from having a reasonable conversation about the topic.

~~~
harperlee
Well from my point of view when I read the link, I see (with a little bit of
characterization) two academics, one of them points at an issue on the
standard statistical approach and tools, provides a reproduceable way to
showcase that answer, and the other one answers “not interesting - i’ll go now
to do real work”. And I found that avoidance of a discussion of a shared
example disheartening.

And Pearl isnt just complaining - the provided an alternative. So it seems
that one side identified a problem, curated examples, delivered a solution,
and now the other side doesnt care about the problem - and causality is not a
trivial problem. As academic discussion, not a business meeting, to me thats
subpar.

That’s only my view from the outside, it may well be the case that it is wrong
and I just need to review more in depth Pearl to see that he does not bring
anything useful.

~~~
bachmeier
They've been debating this for many years, and Gelman is a coauthor and
student of Rubin, who has been debating with Pearl for decades. Gelman is
saying he doesn't want to debate the same issues yet again.

The primary message of Pearl is "all other methods are trash and all empirical
work done using those methods is trash." My interpretation of the post is that
Gelman is arguing other methods are not trash.

I probably agree more with Pearl than Gelman on the details, but Pearl's
approach is just not appropriate for an academic setting.

~~~
evrydayhustling
What they are really fighting about is who gets to reference colloquial
notions of causality when discussing their work. I don't think it's fair to
say that Pearl treats the rest of stats as trash, but he is saying that they
are misleading folks by describing their inference as evidence of cause and
effect - which is about as bad for most academics and especially for the Rubin
crew who have often policed integrity of other inference regimes (like less
rigorous ML).

Debating who gets to use which words is a great way to make sure your debate
only matters to other academics. I'd love to see the causality camp make their
point by unlocking some great new applied results instead!

------
owaty
To counter some of the comments here, I absolutely loved the book and went on
to recommend it to all my scientist friends. While it may get a bit technical
for the lay audience, it should be within reach for a typical scientist or IT
person. I wish our society had a better understanding of causality—that would
raise the level of many important discussions.

Being a long-time fan of Gelman (and having studied his Bayesian Data Analysis
textbook), I am baffled and disappointed that he doesn't seem to understand
Pearl's ideas. In his linked 2009 post[1], he wrote: "I’ve never been able to
understand Pearl’s notation: notions such as a “collider of an M-structure”
remain completely opaque to me." I wonder if, after reading this book
accessible even to non-statisticans, he still doesn't understand it.

[1]:
[https://statmodeling.stat.columbia.edu/2009/07/05/disputes_a...](https://statmodeling.stat.columbia.edu/2009/07/05/disputes_about/)

~~~
mindcrime
_To counter some of the comments here, I absolutely loved the book and went on
to recommend it to all my scientist friends._

Likewise (well, other than not really having any "scientist friends"). I loved
this book, think Pearl has some amazingly valuable ideas, and found the book
relatively accessible even though I'm not a statistician. I won't claim to
have understood every detail on the first reading, but I got enough out of it
to feel like I'll understand it all after a couple of follow on readings, plus
consulting Pearl's other books.

 _I wish our society had a better understanding of causality—that would raise
the level of many important discussions._

Absolutely.

------
DevX101
For anyone interested in this book, I'm going through it now but it addresses
an important question of how to identify causality.

We've all become familiar with the refrain 'correlation does not imply
causation'. This book attempts to answer: 'what DOES imply causation'? He
introduces a framework for how one can answer this question. Not very
mathematically rigorous, but following through the framework does appear to be
able to discover non-intuitive causative conclusions.

Understanding causation will have important implications for the advancement
of A.I. Finding a correlation with the causes hidden in a black box (current
state of deep learning) isn't enough for many disciplines. Doctors for example
will likely need to know WHY an algorithm made a decision, instead of simply
running correlations and telling the operator that a patient has 80% chance of
some diagnosis.

~~~
MAXPOOL
It's possible to infer causation from correlation without experiments if you
add some general assumptions.

One trick in causal discovery is additive noise. If X and Y are noisy
correlating variables and X is causing Y, assumption that the noise in X is
present in Y but not vice versa may reveal the direction of the causal arrow.

Causal Discovery with Continuous Additive Noise Models
[http://jmlr.org/papers/volume15/peters14a/peters14a.pdf](http://jmlr.org/papers/volume15/peters14a/peters14a.pdf)

Nonlinear causal discovery with additive noise models
[https://papers.nips.cc/paper/3548-nonlinear-causal-
discovery...](https://papers.nips.cc/paper/3548-nonlinear-causal-discovery-
with-additive-noise-models.pdf)

Humans seem to have causal reasoning ability that is very ad hoc. It works
well in practice but it's not principled. There is not enough time to do
experiments to establish facts. Correlation is causality seems to be a good
heuristics.

I think that that AI will eventually learn to build causal models in the same
way. Build a quick and dirty causal models with unfounded assumptions and see
what works. Hold multiple effective conflicting causal theories that apply in
different situations without any consistent model.

~~~
ionforce
I feel like I don't have the background to fully understand what you're
saying. Could you explain this in a more lay way?

> X and Y are noisy correlating variables

~~~
randcraw
On average, events X and Y are positively correlated if they usually occur
together even though occasionally they do not. This lack of perfect
correlation is due to A) the natural variation of other (less important)
causal factors, or B) imprecise measurement of their values. A and B are also
known as 'noise'.

All causation implies temporal separation -- causal event X occurs before
caused event Y. The trick is to identify which occurred first AND changed the
frequency of the second.

An example is the assertion: "The presence of rain causes people to carry an
umbrella". Of course, people carry umbrellas even when it doesn't rain, or
don't carry umbrellas when it does rain, but on average, on a day when more
people carry umbrellas than usual, it's usually a rainy day. The scientific
question is: does people carrying umbrellas cause rain? Or does rain cause
people to carry umbrellas?

If the natural variation of rain occurs in some detectable manner (e.g. light
rain vs heavy rain) and you see direct variation in how people carry umbrellas
(less rain thus fewer umbrellas), then it's more likely that rain causes
umbrellas because rain variation correlates positively with umbrella
variation. This is effectively confirmed if on several days you see that more
people are carrying umbrellas than usual but it's NOT raining harder, then
probably carrying of umbrellas does not cause it to rain. (Maybe umbrellas
were being given away for free on that day, or the weather forecast threatened
more rain than actually arrived, causing more umbrellas to be carried.)

Thus when rain amount rises or falls (due to natural variation or noise), you
should see the amount of umbrella carrying follow accordingly. However if the
reverse relationship occurs less often or not at all, this implies that rain
does indeed cause umbrellas, and not the reverse.

~~~
srean
Wouldnt that line of argument lead to believing that drop in barometer
readings cause storms ?

~~~
randcraw
If you disregard noise/variation as an indicator of which event is cause or
effect, then neither of the events you propose is clearly the cause of the
other. Because variation in barometer pressure is likely to be perfectly
correlated with variation in storms, there's no noise/variation in either
event that isn't also present in the other, so neither emerges as more likely
to be the cause of the other.

This strategy of identifying the causal event works only for pairs of
positively correlated events whose variations/noise sometimes do _not_ occur
together, like an increase in umbrellas without an increase in rain.

 _Can_ barometric pressure rise or fall due to causes other than storms? Can
storms arise without being caused by a rise in pressure? I'd say maybe yes to
the first (an elevation change of the meter, or a storm front that passes you
very quickly but whose clouds don't pass directly overhead — maybe). But I'd
say definite no to the second. If you are hit with rain from a storm, your
baro pressure _will_ drop. Thus storms cause pressure to drop, but pressure
drop does not cause storms.

------
notafraudster
As someone who does social science causal inference for a living, I have to
say that I didn't really enjoy "The Book of Why". Full disclosure: I mostly
practice the Neyman-Rubin potential outcomes form of causal inference rather
than the Pearl do-calculus / DAG ("directed acyclic graph") form of causal
inference, but the two are in many cases equivalent.

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.

~~~
breuderink
> 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.

I would be very interested in these references.

~~~
notafraudster
I should say this is not my wing of the world since in social science
typically theory precedes estimation and there would be a strong disciplinary
norm against "I have no idea what causes what". So I don't actually use this
stuff. That being said, I have played with a few of the packages and read a
few pieces on causal discovery.

Jonas Peters et al. - Elements of Causal Inference is a textbook that covers a
little bit of what they called "learning cause-effect models". For algorithms,
check SGS (Spirtes-Glymour-Scheines) and PC (Peter Spirtes and Clark Glymour).
I believe both these algorithms are implemented in R in the package `pcalg`.
There's another R package on BioConductor that implements them too, but I'm
far enough afield from biostats I don't remember the name or have any notes I
can find.

Some recent cites of note: Peters and Buhlmann - "Identifiability of Gaussian
structural equation models" (2014), which led to Ghoshal and Honorio -
"Learning linear structural equation models in polynomial time" (2018) who
generalize the Peters/Buhlmann claim.

Other authors to Google: Dominik Janzig; Joris Mooij; Patrik Hoyer -- all of
these people write papers with the above people, so you should be able to map
out the network.

What the pieces all have in common is that they're trying to establish
empirical differences in the joint distributions of X and Y between scenarios
where X -> Y and where Y -> X. This is only possible in some cases.

Hope this helps.

~~~
mooneater
Is this directly related to learning the structure of PGMs?

eg. [https://arxiv.org/abs/1111.6925](https://arxiv.org/abs/1111.6925) and
practical example at
[https://github.com/jmschrei/pomegranate/blob/master/tutorial...](https://github.com/jmschrei/pomegranate/blob/master/tutorials/B_Model_Tutorial_4b_Bayesian_Network_Structure_Learning.ipynb)

~~~
sjg007
Yes.

------
haberman
As a non-statistician with a lot of interest in statistics, I found the Book
of Why frustrating. Modeling causation seems like an undeniably important step
towards understanding the world better. But the biggest question I had was:
how can you actually verify that your causal model is true? This is not
clearly explained, or wasn't before I gave up on the book. Models are only
useful if we can have some confidence that they correspond to reality.

I was especially interested in the answer to this question, because my only
exposure to the language of "causal chains" has been on Twitter, where they
seemed to serve a distinctly ideological purpose. One (non-mathematical)
person says "I think X is caused by Y", and then a statistician chimes in and
says "you're missing other parts of the causal chain, the real causes are Z
and Q." Where of course, Z and Q are things that one political perspective
prefers to blame, and Y are things blamed by the other side.

For example:
[https://twitter.com/gztstatistics/status/1000914269188296709](https://twitter.com/gztstatistics/status/1000914269188296709).
Here's a great comment from today about the difficulty of establishing
causality in practice:
[https://news.ycombinator.com/item?id=18886275](https://news.ycombinator.com/item?id=18886275)

I want to know how causal chains can be actually proven or falsified, to be
convinced that this isn't just highbrow ideological woo.

~~~
kgwgk
> how can you actually verify that your causal model is true?

This is addressed in the introduction. See box 4 in the flow-chart (“testable
implications”).

“The listening pattern prescribed by the paths of the causal model usually
results in observable patterns or dependencies in the data. [...] If the data
contradict this implication, then we need to revise our model.”

~~~
haberman
The part you removed with ellipses undermines this point:

"These patterns are called "testable implications" because they can be used
for testing the model. These are statements like "There is no path connecting
D and L," which translates to a statistical statement, "D and L are
independent," that is, finding D does not change the likelihood of L."

This says nothing about testing _causality_ , or the direction of causality.
If two things are uncorrelated, then there is probably not a causal
relationship between them, granted. But this is not a very novel or useful
observation.

However if D and L _are_ correlated, the test above says nothing about how to
validate whether D caused L, L caused D, both were caused by a third thing (or
set of things), or the correlation is just coincidence.

For a book whose entire thesis is "causality is rigorous," I expect a much
more rigorous treatment of how to validate causality using more than mere
correlation.

~~~
kgwgk
From your previous comment I understand you didn’t read the whole book so I
don’t know if you got to chapter 4, section ”the skillful interrogation of
nature: why RCTs work.” In short, you can use interventions (i.e. a properly
designed experiment) to verify that the “cause” does indeed produce the
“effect”.

~~~
haberman
RCT's indeed seem like a good way of establishing causality. But RCT's are
well-established, so what is "The New Science of Cause And Effect" (as claimed
by Pearl and MacKenzie) bringing to the table?

Intuitively I might guess that RCT's are the _only_ way of rigorously
establishing cause and effect. I would have been very interested if the book
had confirmed or denied this intuitive conjecture of mine.

Another comment in this thread claims that you can infer causality without
intervention:
[https://news.ycombinator.com/item?id=18884104](https://news.ycombinator.com/item?id=18884104)
Perhaps this is true?

This is the kind of discussion that I wish the book had focused on. I want to
probe at the line between belief and established fact, and understand what we
can rigorously say given the evidence we have. I have a strong aversion to
reading extended flowery descriptions of big ideas if the speaker has not
rigorously shown that the model maps to the real world. Otherwise it's like
listening to just-so stories.

~~~
kgwgk
This is the kind of discussion the book focuses on, you should try to read it.
RCTs are not the only way to answer these questions and observational data can
be used in some cases (but note that the validity of the inference is
conditional on the model being correctly specified).

~~~
haberman
Maybe I should put some more effort into the book. But statements such as this
make me extremely wary:

> but note that the validity of the inference is conditional on the model
> being correctly specified

This strikes me as begging the question. The model is exactly what I don't
trust unless it is rigorously justified, so anything conditional on the model
being correctly specified I _also_ don't trust.

It all feels like a house of cards.

~~~
kgwgk
What kind of answer do you expect?

You can get no causality from data alone. You always need additional
assumptions.

If you can do an intervention and manipulate a variable as you wish, the
assumption of its independence is warranted. A correlation with the outcome
indicates a causal path (or you're being [un]lucky). Even in that case a more
complex causal model is useful to get better estimates, distinguish direct and
mediated effects, etc.

If you have observational data only there is not much that can be done without
a causal model. Given a model, the causal effect of one variable on another
may be estimated in some cases. But if your model is wrong you may conclude
that there is an effect when none exists or deny the existence of a real
effect.

~~~
haberman
I think I expected the book to better live up to its billing. Here is an
excerpt from the in-sleeve summary:

"Beginning with simple scenarios, like whether it was rain or a lawn sprinkler
that got a sidewalk wet, the authors show how causal thinking solves some of
today's hardest problems, including: whether a drug cured an illness; whether
an employer has discriminated against some applicants; and whether we can
blame a heat wave on global warming."

In all of these "hard problems", it is the model itself that is the most
contentious piece, and the most ideological. Some people have a mental model
where CO2 produced by humans is causing climate change (which I agree with),
and others believe that the changes can be explained by natural fluctuation.
These beliefs are undoubtedly influenced by a person's biases. It's not very
useful to say "once you have accepted a causal model, you can draw lots of
useful inferences." Because the main point of contention is over what is
causing what.

I found this statement of yours honestly more useful than anything I read in
the book so far: "You can get no causality from data alone. You always need
additional assumptions." The downside of this is that different people can
make different assumptions, and so this implies that this kind of causal
analysis can't mediate disagreements between different groups of people who
see the world very differently.

~~~
kgwgk
> It's not very useful to say "once you have accepted a causal model, you can
> draw lots of useful inferences." Because the main point of contention is
> over what is causing what.

Well, accepting a causal model and drawing lots of useful inferences seems
better than drawing lots of misleading inferences because no attention is paid
to the model (or being unable to make any inference because it's not obvious
how the data can be used).

Even if people may not agree on what is the right model at least this approach
makes the model explicit. And in many cases there is no reason for
disagreement, but if there is no careful analysis the wrong model may be used
by mistake. For example, chapter 8 has an extense discussion of the potential
outcomes approach in the context of salary as a function of education and
experience.

------
svat
A discussion about this book on the statistics StackExchange, with some
interesting answers: "The Book of Why by Judea Pearl: Why is he bashing
statistics?" [https://stats.stackexchange.com/questions/376920/the-book-
of...](https://stats.stackexchange.com/questions/376920/the-book-of-why-by-
judea-pearl-why-is-he-bashing-statistics)

~~~
nkurz
Yes, that has lots of great stuff in the comments. One of them (convolutedly
just added based on a link provided in a comment on the Gelman blog post) was
to this review: [https://www.kdnuggets.com/2018/06/gray-pearl-book-of-
why.htm...](https://www.kdnuggets.com/2018/06/gray-pearl-book-of-why.html).
The review, and then back-and-forth at the bottom and in comments between Gray
and Pearl are wonderful context.

~~~
kgwgk
The irony in this comment is priceless:

“Simpson’s paradox in its various forms is something that generations of
researchers and statisticians have been trained to look out for. And we do.
There is nothing mysterious about it. (This debate regarding Simpsons, which
appeared in The American Statistician in 2014, and which I link in the
article, hopefully will be visible to readers who are not ASA members.)”

There is nothing mysterious about Simpson’s paradox but the proper answer is
still being debated!

Pearl’s response ends as follows:

“The next step is to let the community explore:

1) How many statisticians can actually answer Simpson’s question, and

2) How to make that number reach 90%.

I believe The Book of Why has already doubled that number, which is some
progress. It is in fact something that I was not able to do in the past thirty
years through laborious discussions with the leading statisticians of our
time.

It is some progress, let’s continue.”

[http://causality.cs.ucla.edu/blog/index.php/2018/06/15/a-sta...](http://causality.cs.ucla.edu/blog/index.php/2018/06/15/a-statisticians-
re-reaction-to-the-book-of-why)

------
banana_man
Pearl's causal model is a decent didactic tool, but it seems to have little
relevance when it comes to trying to figure out real-world problems. (If any
actual scientific breakthroughs have come from Pearl's approach, I'd love to
hear about them. I don't think there are any.) I believe the disagreement
between Gelman and Pearl may come down to the fact that Gelman often deals
with actual estimation problems where you have to find causal parameters using
scanty and imperfect data, with no strong theory to guide decisions, while
Pearl is focused on toy models where all the limitations and uncertainties of
real problems are assumed away.

Donald Rubin has said, surely correctly, that design trumps analysis in causal
inference. Pearl's approach seems to be the opposite--all focus is on the
analytical details. For practicing scientists, I think this article this
article provides a much more useful model for causal inference:
[https://academic.oup.com/ije/article/45/6/1787/2617188](https://academic.oup.com/ije/article/45/6/1787/2617188)
See Textbox 3 where different approaches to studying the relation between
smoking and low birthweight are described. The various approaches rely on
different assumptions and any one study design may not be convincing by
itself, but the way their results converge ("triangulation") is very
convincing. AFAIK, none of the studies used DAGs, yet the causal evidence
provided is stronger than any DAG could provide.

~~~
kgwgk
The formal approach to quantitative causal inference in epidemiology:
misguided or misrepresented?

[https://academic.oup.com/ije/article/45/6/1817/2960059](https://academic.oup.com/ije/article/45/6/1817/2960059)

There are other comments, and the authors’ reply:

[https://academic.oup.com/ije/issue/45/6](https://academic.oup.com/ije/issue/45/6)

------
srean
> "I’ve never been able to understand Pearl’s notation:" \-- Gelman.

That should not be surprising:

"It is difficult to get a man to understand something when his salary depends
upon his not understanding it." \-- Upton Sinclair

Accepting Pearl would amount to stating that some of the procedures
we(statisticians) have been using, championing and sourcing funds for, for
half a century are seriously flawed. Thats going to have consequences on
future funding. Of course there will be resistance.

Tony Hoare is worth paraphrasing -- some methods are so crisp and small that
they are obviously correct. Others are so complex that one cannot find obvious
errors. Piling on a hierarchy of random variables upon random variables and
parameters upon prameters lies firmly in the latter class.

This is actually a charitable analogy because some uses of statistical methods
are incorrect but the error lies in incorrect use -- Using a tool or a
technique to answer a question that it cannot answer. Smothering it with
complexity and phrases like 'but real world', 'but noisy big data' helps to
muddy the waters enough to deflect the attention from the fundamentals
difference beween conditioning and intervening.

I can be sympathetic to a claim that a method is more effective solving a
complicated problem than a simple one. On the otherhand, if it turns out that
the body of theory on which the proposed method has been built, the same
proposed method that is presumably correct for the complicated case, cannot
deal with a pedagogic toy scenario correctly, that raises my eyebrows.

------
digitalzombie
I tried reading Pearl once. I couldn't get over his tone.

Andrew Gelman summarize it pretty nicely his take on it.

Coming from a statistic background, casual inference is a growing thing now
and several government sponsor research have been pushing for it.

Casual inference from statistic point of view is base on missing data,
basically Rudin stuff. It's pretty dang interesting to me. I'm sure there are
many ways of looking at the same thing. Linear regression you can look at it
in more of a optimal math problem with cost function or you can look at it in
statitic using maximum likelihood estimation. Both have it's pro and con, with
MLE you get a confidence interval. In my bias opinion I feel that statistic is
only about data and it's a great domain for casual inference.

There's no need to put a field down to make yours better. But if it's
constructive criticism (pro/con, contrast) I think it make both fields better.
Pearl attitude is off putting when you try to read his stuff. We're all human
and have vary degree of ego, if you're going to try to convince us that do
calculus and your ways is better be objective about it or word things better.
If you don't want to convince people then just be blunt as hell.

------
drallison
Judea's work and "The Book of Why" ought to be required reading for anyone who
draws conclusions from data. People who do not understand statistics well
enough to understand the book need to study statistical thinking until they
do.

Michael Nielson has a nice post (circa 2012) on the topic at
[http://www.michaelnielsen.org/ddi/if-correlation-doesnt-
impl...](http://www.michaelnielsen.org/ddi/if-correlation-doesnt-imply-
causation-then-what-does/) with comments at
[http://www.michaelnielsen.org/ddi/guest-post-judea-pearl-
on-...](http://www.michaelnielsen.org/ddi/guest-post-judea-pearl-on-
correlation-causation-and-the-psychology-of-simpsons-paradox/).

------
stewbrew
So, AG writes a sloppy review to complain about a sloppy mischaracterization
of (mostly classic) statisticians.

"About the exposition of causal inference, I have little to say."

That would have been interesting though.

------
ta1234567890
Why is not a physical, mathematical or scientific question, but rather a
philosophical one.

Personally I believe there is no why (no causality at all). Rather we love to
think it exists because it reduces our uncertainty. It's too much to accept
our whole reality is just a bunch of random coincidences.

------
baron816
I tried to listen to the audiobook of this. Terrible idea. One of the few
audiobooks I’ve abandoned.

~~~
lolptdr
Why was it bad? I'm not an audiobook listener so I don't know what's
considered bad.

~~~
ffn
It's an user-experience problem: for an audiobook, Book of Why is quite dense
in formula that are hard to visualize when spoke as words.

For example, consider what happens if we try to describe a causal diagram in
words

"A points to B, A points to X, B points to Y, and X points to Y. Now, if we
apply do(X) to the diagram, we see that we can Y is now no longer a child
of..."

or even simple formulas in words:

"P of A given B times P of B is equal to P of B given A times P of A"

For most of us, this sort of deal is hard to "get" and would be much better
served if we just looked at a visual diagram or saw the equation.

I personally had to repeat many sections over and over again with a notebook
and pencil in hand to truly understand what was being read to me... but if I'm
taking notes and creating visuals for myself, then I might as well have just
gotten the paper variant of this book lol.

------
kgwgk
Relevant online course: [https://www.edx.org/course/causal-diagrams-draw-
assumptions-...](https://www.edx.org/course/causal-diagrams-draw-assumptions-
harvardx-ph559x)

------
dmolony
"Fisher would lager argue"

I can't tell if this is a typo in the original text, or a typo from the person
complaining about a lack of care.

