From my experience it pays to learn how to think about causal inference like Pearl (graphs, structural equations), and also how to think about causal inference like Rubin (random variables, missing data). Some insights only arise from a synthesis of those two views.
Pearl is a giant in the field, but it is worth remembering that he’s unusual in another way (compared to a typical causal inference researcher) -- he generally doesn’t worry about actually analyzing data.
---
By the way, Gauss figured out not only the normal distribution trying to track down Ceres’ orbit, he actually developed the least squares method, too! So arguably the entire loss minimization framework in machine learning came about from thinking about celestial bodies.
Some reading on this:
https://csss.uw.edu/files/working-papers/2013/wp128.pdf
http://proceedings.mlr.press/v89/malinsky19b/malinsky19b.pdf
https://arxiv.org/pdf/2008.06017.pdf
—
From my experience it pays to learn how to think about causal inference like Pearl (graphs, structural equations), and also how to think about causal inference like Rubin (random variables, missing data). Some insights only arise from a synthesis of those two views.
Pearl is a giant in the field, but it is worth remembering that he’s unusual in another way (compared to a typical causal inference researcher) -- he generally doesn’t worry about actually analyzing data.
---
By the way, Gauss figured out not only the normal distribution trying to track down Ceres’ orbit, he actually developed the least squares method, too! So arguably the entire loss minimization framework in machine learning came about from thinking about celestial bodies.