# 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"

• What exactly do you mean saying „causal“? Does Ridge/Lasso count as non-causal? Nov 8, 2021 at 20:47
• @Peter Unfortunately not, Ridge and Lasso doesn't explain casuality. What I mean by my question is the following: Y = aX + bZ => for sipmplicity let's take they are linear Does X explain Y ? Does Z explain Y? Does Z explain Y through X? Or does Y explain X or Z than the model is literally off. I want to respond to these questions, not just by using correlation which is false. Nov 12, 2021 at 15:05
• Geri, are you able to use Granger causality test? Or maybe the predictive power score? towardsdatascience.com/… Nov 12, 2021 at 15:56
• Granger causality is for time series data en.wikipedia.org/wiki/Granger_causality. I‘m not aware that there is a test etc to detect causality in the narrow sense. So when you mean „identification“ and not only „exogenous variation“ when you speak about causality, things get little messy fmwww.bc.edu/EC-P/wp957.pdf Nov 12, 2021 at 19:34
• Does the Paul Holland's motto given in this question stats.stackexchange.com/questions/2245/… answer your question? Nov 13, 2021 at 8:17