[FEATURE] Import the Empirical Calibration package to adjust

[talgalili]

The Empirical Calibration package, developed by Google, provides a method to compute empirical calibration weights using convex optimization. This approach balances out the marginal distribution of covariates directly while reducing the inflation of variance. This is similar to performing raking while trying to keep the weights to be as equal as possible. It offers a bias correction solution that resembles the raking and CBPS methods that are implemented in the balance package.

It might be worth importing it into balance in the future.

Reference

Title: A Python Library For Empirical Calibration
Authors: Xiaojing Wang, Jingang Miao, Yunting Sun
Year: 2019
Journal: arXiv preprint arXiv:1906.11920
URL: https://doi.org/10.48550/arXiv.1906.11920