
The Causal Revolutionary – Interview with Judea Pearl - onuralp
https://www.3ammagazine.com/3am/the-causal-revolutionary/#.W5zZAEtAHvs.twitter
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celrod
For those interested in Causal Inference, here is Judea Pearl's fairly
accessible intro "An Introduction to Causal Inference":
[http://ftp.cs.ucla.edu/pub/stat_ser/r354-corrected-
reprint.p...](http://ftp.cs.ucla.edu/pub/stat_ser/r354-corrected-reprint.pdf)

~~~
mlthoughts2018
It’s important to note this is causal inference from Pearl’s darling theory
using do-calculus and graphical models.

I work in causal inference professionally (large scale causal impact
measurement for observational studies that customers require causal
interpretations of, for clinical trial data and advertising data mostly).

In practice, the stuff from Pearl is just unhelpful, and it’s very valuable to
also read sources from Rubin, Imbens, Gelman and many others, because there
are whole other approaches to the problem, approaches which more or less
completely sidestep and have no need for do-calculus concepts, but which are
extremely successful in practice, with real observational data sets.

Methods like propensity matching, propensity weighting, and hierarchical
models using different treatment levels as strata, are absolutely critical for
applying this stuff in the real world.

If you only ever read Pearl, you’ll mistakenly think those methods are somehow
“inferior to” or “subsumed by” do-calculus when this is not at all true,
certainly not in practice.

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mycelium
_there are whole other approaches to the problem... which are extremely
successful in practice, with real observational data sets._

I'm going to echo my sibling commenter, would love to hear it if you have any
pointers on where to start reading.

I'm a software engineer but data work comes up all the damn time. Seeing
coworkers approach causality in a hand-wavy way without knowing a more
rigorous approach to suggest is frustrating! I had hoped Pearl's book was
exactly that.

~~~
mlthoughts2018
See my response to the other comment, <
[https://news.ycombinator.com/item?id=18002046](https://news.ycombinator.com/item?id=18002046)
>.

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mathgenius
> The equations of physics are algebraic and symmetrical, whereas causal
> relationships are directional.

I don't agree with this. Quantum measurements are projective and they are very
much one way. People seem to want to dismiss this as being just "epistemic"
like the way entropy (thermodynamics) is one-way, but not fundamentally one
way. Entropy increases only because we can't see all the details. Quantum
measurements are not like that.

~~~
antidesitter
Many physicists believe quantum “measurements” are just an approximation to
underlying unitary processes. See
[https://news.ycombinator.com/item?id=17894779](https://news.ycombinator.com/item?id=17894779)
for more discussion. See also
[https://en.wikipedia.org/wiki/Quantum_decoherence](https://en.wikipedia.org/wiki/Quantum_decoherence).

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thallukrish
This looks very interesting. I always felt most of ML is about one aspect of
intelligence - prediction from existing data. While this may work for specific
tasks like driving a car or analysing a scanned image, the human intelligence
has always been about the Why? on anything. Answering the Why? on a pile of
data by connecting the dots using a causal model is also prediction in a
sense, but a more generic one than a prediction on a bunch of specific classes
or outcomes. For example, a self-driving car algorithm trained on a pre-
classified data to detect obstacles versus a algorithm that can answer Why
something is a obstacle is vastly different and is much more effective I
guess.

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YeGoblynQueenne
>> JP: Correct. Formally, Bayesian networks are just efficient evidence-to-
hypothesis inference machines. However, in retrospect, their success emanated
from their ability to “secretly” represent causal knowledge. In other words,
they were almost always constructed with their arrows pointing from causes to
effect, thus achieving modularity. It is only due to our current understanding
of causality that we can reflect back and speculate on why they were
successful; we did not know it then.

Hang on a sec. If Bayesian networks are perfectly capable of representing
causality relations, and in fact they've been doing just that all along
(albeit "secretely") then why the hell do we need a different formalism to
represent causality?

To give an analogy - if we can represent context-free langugaes with regular
automata, then what's the point of context-free languages? Instead, we
classify languages that can be represented by both regular automata and
pushdown automata as regular, and reserve the context-free designation for
languages that _cannot_ be represented by regular (or finite) automata.

In the same way, if causality relations can be represented by Bayesian
networks, then higher-order representations are not really needed, or must be
reserved for some object that Bayesian networks can't represent.

In any case, this is just a huge piece of ret-con. Bayesian networks always
represented causality relations, only they did so "secretly"! That's up there
with the original Klingon's flat heads being the result of a virus infection;
or how Jean Grey didn't really die and it was the Phoenix Force that had taken
her form.

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YeGoblynQueenne
>> The astonishing success of big-data and machine learning reflects our
under-estimating how much can be achieved by the low hanging fruits of model-
free curve-fitting. But when we look at the limitations unveiled by the
calculus of causation we understand that human-level AI requires two more
layers: intervention and counterfactuals.

Yes, well, the problem with that is that the vast majority of researchers in
AI know very well that human-level AI is many, many years away still. Whereas
those "low-hanging fruit"? They're just hanging there, ripe for the picking
and large companies are very eager to throw a shitload of money at people who
can pick them, _right. now_.

And- let's be fair. Anyone who knows how to do this "model-free curve-fitting"
that the good professor so despises has a brilliant career for upwards of 30
years laid out for them- and those are 30 years in which they won't have to
think about causality and Judea Pearl _even once_.

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woodandsteel
Very interesting. As someone with a background in philosophy, I am wondering
if causality has been excluded because of mistaken metaphysical and
epistemological assumptions when the sciences were originally developed.

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mark_l_watson
Good interview- I also recommend his very latest book: very approachable
material.

