
Causal inference in Python - aleyan
https://github.com/akelleh/causality
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dash2
I'd love for someone to explain about this "inferring causality from
observational data". I've read a little before, and it sounded really
exciting, but I wondered if it was just the same as instrumental variables
with multiple instruments. Anyone know more?

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closed
Statistical modeling often involves two parts, model selection and model
inference. As it turns out, many models have a causal interpretation. For
example, a simple linear model could be interpreted as x causes y using the
equation y = b*x + error.

If you get a bunch of variables and relate them through linear equations where
some cause others plus some error, then different patterns of causal relations
imply different covariance matrices. Classically, people have used these
covariance matrices to choose between possible causal models.

There are different approaches, but a common one in the behavioral sciences is
to choose a few causal models to represent theories, and then perform model
selection (like choosing between multiple regression models with different
variables).

To answer your question, though, instrumental variables are a specific causal
pattern in a model, but there can be other models, such as those with latent
variables.

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ertand
There is also Tetrad. It's written in Java and has a GUI.
[https://github.com/cmu-phil/tetrad](https://github.com/cmu-phil/tetrad)

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ebola1717
Haha, was wondering if this was by Adam Kelleher. I've been to a few of the
meetups he hosted about causal inference. Really smart guy.

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rrggrr
I always wish the readme included a real-world example to help make the
libraries more accessible.

