The balance python package offers a simple workflow and methods for dealing with biased data samples when looking to infer from them to some target population of interest.
Regarding balance/graviton (once we deal with the glmnet->sklearn transition).
How about we just move to using max_de to be based on weight trimming only (instead of shrinkage in the LASSO lambda stage)?
And if the results using weight trimming (for some max_de), are tolerable (similar to existing solution), then transitioning to it / maintaining it should be easier than the current CV solution.
Regarding balance/graviton (once we deal with the glmnet->sklearn transition).
How about we just move to using max_de to be based on weight trimming only (instead of shrinkage in the LASSO lambda stage)? And if the results using weight trimming (for some max_de), are tolerable (similar to existing solution), then transitioning to it / maintaining it should be easier than the current CV solution.
TBD...