Algorithms for casual feature selection for continuous Y

Currently I have been trying to find some good algorithms for feature selection. Using correlation or other non casual type of method will not be the right way to do a feature selection. I'm am searching for aglorithms in python or libraries that use casual effects for feature selection. Currently there are only for binary outcomes, I'm searching for a regression problem so it must be continuous.

Causality-Guided Feature Selection

Topic feature-construction feature-extraction feature-selection python

Category Data Science

1
answered at

The best resource I've found for ready-made causal inference implementations is this Github repository. I've personally used R and Python implementations of Tetrad to create a graph of the features, and then code an additional step to get variables within the Markov Boundary with respect to the target variable for feature selection. You could also use variants of the PC algorithm to achieve the same thing. There's also this review paper that outlines a lot of the algorithms for different cases.

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