
Causal Inference and Data-Fusion in Econometrics (2019) - viburnum
https://arxiv.org/abs/1912.09104
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psandersen
I suspect we'll see causal techniques start merging with more traditional
AI/ML tools over the coming years.

Causal forests are an example that extends random forests, but I imagine a lot
of the value in current pipelines would be to use causality as regulariser.
This could be a parameter that controls the weight of established causal
links, or it could be as a scaffold; e.g. a first 'causal pass' is used to
establish constraints (monotonicity, conditional variable selection, reject
changes that result in predictions inconsistent with the initial causal model
when there is a strong causal model etc).

RL is likely more promising. If agents could be made to search for causality
in an environment these relationships could be made much harder to unlearn
which would then enable more efficient exploration & incremental learning.
Framed this way causality guides attention, limits the search space and locks
in learning.

I've got some quarantine reading/experiments to try! :)

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6gvONxR4sf7o
I totally agree as causation as a sort of generalization enhancer akin to
regularization. In stats, there's the notion of some "true" parameter that's
trying to be estimated, but you get all sorts of systematic errors creeping in
if you estimate it wrong. If you get a good estimate of it, though, you've
learned something "true" and that generalizes much better than systematically
wrong versions. Like, _if_ you figure out F and m and that F=ma, you're going
to make really good predictions, regardless of how far from your original
training you are. Other truths are still pretty limited (like the example of a
social study finding the true treatment effect of something on affluent white
20 year olds in LA), but the scientific ideas of internal and external
validity still apply quite nicely.

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cschmidt
If you're wanting to read more about causal inference, I liked this flowchart
on "Which causal inference book you should read"

[https://www.bradyneal.com/which-causal-inference-
book](https://www.bradyneal.com/which-causal-inference-book)

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keithyjohnson
The Book of Why by Judea Pearl may be a good starting point for anyone
interested in this.

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littlestymaar
The topic is really interesting, and this book had a big impact in my
understanding of the world, yet I found it pretty annoying to read. The grudge
born by J. Pearl against the statistics community who rejected his ideas is
way too present in the book IMHO. He's almost like “I was right all along you
fuckers, who's the boss now!” on every single page, and I feel it's really
disserving his ideas.

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rhombocombus
That was what put me off about that book too. I was really excited to learn
about his math, but the continuous hard sell combined with attacking
traditional statistical methods (which still have a lot of use) was pretty off
putting. I will probably pick it back up, but it was not a great way to hook
readers or bring them around to your way of thinking.

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troelsSteegin
Elias Bareinboim [1] mentions "we are beta-testing a tool called ‘Fusion’,
which offers an easy-to-use way of doing causal inference from 1st principles"
[2]. Tantalizing, but I've not yet seen anything else about 'Fusion'.

[1] [https://causalai.net/](https://causalai.net/) [2]
[https://twitter.com/eliasbareinboim/status/11916094504628838...](https://twitter.com/eliasbareinboim/status/1191609450462883841)

~~~
troelsSteegin
A tool from Bareinboim is mentioned in a Technology Review article [1] on
causal reasoning in AI. Per the article Pearl considers Bareinboim to be a
protege.

quoting: "One of his systems, which is still in beta, can help scientists
determine whether they have sufficient data to answer a causal question.
Richard McElreath, an anthropologist at the Max Planck Institute for
Evolutionary Anthropology, is using the software to guide research into why
humans go through menopause (we are the only apes that do).

The hypothesis is that the decline of fertility in older women benefited early
human societies because women who put more effort into caring for
grandchildren ultimately had more descendants. But what evidence might exist
today to support the claim that children do better with grandparents around?
Anthropologists can’t just compare the educational or medical outcomes of
children who have lived with grandparents and those who haven’t. There are
what statisticians call confounding factors: grandmothers might be likelier to
live with grandchildren who need the most help. Bareinboim’s software can help
McElreath discern which studies about kids who grew up with their grandparents
are least riddled with confounding factors and could be valuable in answering
his causal query. “It’s a huge step forward,” McElreath says."

... looks "marginal" if you ask me. But I can't think of a better beta-tester
than McElreath for this.

[1] [https://www.technologyreview.com/2020/02/19/868178/what-
ai-s...](https://www.technologyreview.com/2020/02/19/868178/what-ai-still-
cant-do/)

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benibela
I have been working on a PhD about causal graphs for the last 6 years

I wonder what the career perspectives that brings? I want to do no statistics,
only programming

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ssivark
Lol, don’t you think it has applications to _every_ context? :-)

What’s your PhD work on?

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benibela
I studied the standard models, they are very limited. Causes and effects as
DAGs, so there cannot be any cycles or feedback loops. And some assumptions
fail when there are deterministic relationships.

I investigated adjustment sets and instrumental variables.

