
The Case for Causal AI - csabourin
https://ssir.org/articles/entry/the_case_for_causal_ai
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ghj
As a layman, is causality worth studying?

For example, can I answer questions like what are the main causes that are
moving corona stats?

With intervention I suppose you can conduct an experiment where you (randomly)
pick cities and make half of them wear masks and half of them not. But this is
of course unethical! And some stuff you want to know the effects of (e.g., how
did gatherings at protests affect the infection rate) are one time events that
can't be replicated again.

So all we have left are lots of natural experiments. Different
countries/states/communities are handling the situation differently with a
wide range of outcomes. No two communities are directly comparable since they
differ along many other dimensions other than corona policy. But as a human I
am still drawing plenty of conclusions on what caused what and to what degree.
So it seems like a solvable problem. How do I make it rigorous?

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cgearhart
It’s interesting to study, but it doesn’t magically make hard problems
tractable. It seems to make certain common mistakes less likely. When you do
statistical analysis or machine learning to model p(x,y) or its friend p(y|x)
it’s possible that you mistake correlations for causation (because there is no
way to express directionality of dependency). If I have data about the
measurement on a barometer and atmospheric pressure then I may learn a model
that predicts higher pressure when the barometer reading is high. But if I
manually force the barometer reading higher that doesn’t increase the
atmospheric pressure. Causality provides a mathematical framework to express
that idea.

The problem I have with causality is how to find accurate causal diagrams from
unstructured observational data. We _could_ guess and check every possible
causal relationship, but that’s at least exponentially hard—in which case
causality is useless.

People seem to have reasonably good capabilities for generating candidate
causal hypotheses from observational data (basically all of modern science),
but most of the material I’ve found on causality focuses on the theoretical
benefits it provides rather than on practical applications at scale. (I don’t
care if we can automate finding the causal graph for barometer & air pressure;
how do I find a causal graph for classifying fake/real news from plain text
data?)

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memexy
I guess it's tricky because the real world is full of feedback loops. If you
want a causal model for fake news then your model needs to include some
representation of incentives for ad revenue and clickbait. How does the causal
inference framework handle feedback loops?

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wenc
I believe DAG-based causal inference isn’t able to handle feedback loops
(acyclic) or nonlinearity (linear). Nonlinearities include stuff like
deadbands and delays.

Control theory models handle these things just fine. But control models are
hard to apply to sociological/epidemiological domains, where causal inference
dominates.

From what I gather, causal inference is useful for designing studies. I’m not
sure if they’re used for prediction — would appreciate if someone in the know
could chime in.

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marmaduke
> DAG-based causal inference isn’t able to handle feedback loops (acyclic) or
> nonlinearity (linear)

I don't think this should be true, and if it is, then "causal inference"
should be qualified to refer only to a specific modeling framework. As a
counter example, it's possible to formulate a nonlinear differential equation
model of Covid spread and infer the parameters to construct a plausible,
causal, generative model.

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memexy
The article outlines two approaches to _causal AI_

> There are two approaches to causal AI that are based on long-known
> principles: the _potential outcomes framework_ and _causal graph models_.
> Both approaches make it possible to test the effects of a potential
> intervention using real-world data. What makes them AI are the powerful
> underlying algorithms used to reveal the causal patterns in large data sets.
> But they differ in the number of potential causes that they can test for.

Does anyone have references and tutorials for either approach?

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pougetj
Imbens and Rubin’s book “Causal Inference for Statistics, Social, and
Biomedical Sciences: An Introduction” is an excellent reference for the
Potential Outcomes approach. There are also several summaries online done by
Rubin which do a great job of explaining the core concepts and how they apply
in concrete examples. Rubin’s class in grad school was a large factor in my
decision to focus my PhD on Causal Inference, and that book is one I return to
frequently.

~~~
memexy
Thanks.

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ilaksh
A recent thing I was learning/thinking about using neural networks to do
program synthesis (to try to match some kind of fine-grained data), then run
the program and see the actual output. Something similar could probably work
for other types of symbolic computation. Let the neural network give you an
intuition in a format you can actually check. So its basically there to speed
up the concrete search/analysis.

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memexy
How does having a program / algorithm and checking it on various input values
help with understanding causality?

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ilaksh
Neural networks are largely black boxes whereas a computer program or other
symbolic configuration is a compositional interpretable description.

The program is the explanation of the output, i.e. the program causes the
output.

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memexy
But why is that a causal explanation? If I can write down a simulation of
planetary motion then that doesn't necessarily explain the causal mechanism
behind why the planets actually move. In fact, there are simulations for
planetary motion and none of them are causal explanations because they don't
actually move the planets.

