
Causal Inference Book - luu
https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/
======
kgwgk
Also in the recent and upcoming releases in "causality" department: The Book
of Why: The New Science of Cause and Effect (Judea Pearl, Dana Mackenzie)
[https://www.nytimes.com/2018/06/01/business/dealbook/review-...](https://www.nytimes.com/2018/06/01/business/dealbook/review-
the-book-of-why-examines-the-science-of-cause-and-effect.html)

~~~
org3432
Judea Pearl also wrote a summary of The Book of Why here:
[http://ftp.cs.ucla.edu/pub/stat_ser/r481.pdf](http://ftp.cs.ucla.edu/pub/stat_ser/r481.pdf)

------
forapurpose
> Jamie Robins and I are working on a book that provides a cohesive
> presentation of concepts of, and methods for, causal inference. Much of this
> material is currently scattered across journals in several disciplines or
> confined to technical articles. We expect that the book will be of interest
> to anyone interested in causal inference, e.g., epidemiologists,
> statisticians, psychologists, economists, sociologists, political
> scientists, computer scientists...

I'm a bit surprised that there isn't already a "cohesive presentation", both
for teaching and as a resource for practitioners. (Of course, that seems to be
when books are written, when the need suddenly seems obvious.) Would any
scientists, etc. comment on how the topic is currently taught and what
reference is used? Or am I misunderstanding the nature of the topic, such as
its significance or generality? Has something changed?

~~~
imh
Typically, stats seems to have different usage and teaching by field.
Psychologists have their flavor, physicists another. Econometricians,
epidemiologists, nutritionists, etc, all different. They're pretty much all
asking causal questions, but they're coming up with different techniques to
solve the specific weird questions that are unique to their field.

Some fields get to randomize everything. Causal inference is is as simple as
asking "is this difference real" when the difference in your experiment would
be causal by design. This is what is taught as stats 1 to everybody who starts
learning stats.

Many fields don't get to randomly do things :(. Maybe you want to know the
effect of a disease, but we as a society have decided that harming people to
learn for the eventual greater good is bad (hello "rush out the self driving
cars!" proponents!). Or maybe randomizing would involve merging a few multi-
billion dollar companies and grad students don't have that budget. For
whatever reason, you can't intervene in the same way. I give those two
examples because epidemiologists and economists have really extended the state
of the art of causal inference because their data doesn't look the same as
many other fields. Inference is harder (but still possible!).

Those niche developments have been happening for a while, but haven't been
cohesively un-niched. Big (non-randomized) data is becoming a big thing and
everyone wants to use it to make decisions. Decisions require causal
statements and most people only know how to make those statements for cleanly
randomized stats 1 stuff. People seem to be picking up on the fact that some
of this previously niche stuff is much more broadly useful.

~~~
forapurpose
Thank you.

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westurner
Causal inference (Causal reasoning)
[https://en.wikipedia.org/wiki/Causal_inference](https://en.wikipedia.org/wiki/Causal_inference)
(
[https://en.wikipedia.org/wiki/Causal_reasoning](https://en.wikipedia.org/wiki/Causal_reasoning)
)

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forkandwait
I bet someday we realize that "causality" is about as real as the ether or
force at the subatomic level. That is, something that seems fundamental and
obvious but is really just wrong.

~~~
imh
It may be wrong in the sense that free will is wrong, but a lack of free will
doesn't stop me from making decisions. Causality is all about counterfactuals,
and the admission is right there in the name counter-factual. We are already
aware.

The most popular framework for causality talks about potential outcomes. If I
took my blood pressure meds now (all else equal), what would my blood pressure
be at noon? If I skipped my meds today (all else equal), what would my blood
pressure be at noon? That tells me the casual effect of the meds on my blood
pressure today. But I either will or won't take the meds. One of those
branches is simply wrong on a fundamental level, like you say. "What would
happen" is in some sense meaningless. But it's sure as shit useful to think
about, and that's the whole point.

Said differently, even in a toy model of the universe where everything is
deterministic and causality is meaningless on a physics level, causality can
be usefully defined on a practical level. Counterfactuals are counter factual,
but that's ok.

~~~
forkandwait
In a toy model, the ether and Aristotlean mechanics worked ok too. I am not
saying we should give up the notion altogether, but that causality as we
intuit it may not be the best way to understand the universe.

~~~
imh
True. Causation is less useful for fundamental physics than it is for the
other sciences. The intent of my comment is that even if causation is
nonsensical at the physical level, it may be usefully and sensibly defined at
higher other levels. Just like how "true" randomness isn't a prerequisite for
usefully defining probability.

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iron0012
This is a great book, much more practical for the applied researcher than
anything written by Pearl.

