Dealing with biased data samples is a common task across many statistical fields. In survey sampling, bias often occurs due to the unrepresentative samples. In causal studies with observational data, the treated vs untreated group assignment is often correlated with covariates, i.e., not random. Empirical calibration is a generic weighting method that presents a unified view on correcting or reducing the data biases for the tasks mentioned above. We provide a Python library EC to compute the empirical calibration weights. The problem is formulated as a convex optimization and solved efficiently in the dual form. Compared to existing software, EC is both more efficient and robust. EC also accommodates different optimization objectives, supports weight clipping, and allows inexact calibration which improves the usability. We demonstrate its usage across various experiments with both simulated and real-world data.
Wang, Xiaojing, Miao, Jingang, and Sun, Yunting. (2019). A Python Library For Empirical Calibration. arXiv preprint arXiv:1906.11920.
The easiest way is propably using pip:
- To install from github:
pip install -q git+https://github.com/google/empirical_calibration
- To install from pypi:
pip install empirical-calibration
If you are using a machine without admin rights, you can do:
pip install -q git+https://github.com/google/empirical_calibration --user
If you are using Google Colab, just add "!" to the beginning:
!pip install -q git+https://github.com/google/empirical_calibration
Package works for python 3.6 or later.
Package can be imported as
import empirical_calibration as ecThe best way to learn how to use the package is probably by following one of the notebooks, and the recommended way of opening them is Google Colab.
- Survey calibration
- Causal inference
Package is created and maintained by Xiaojing Wang, Jingang Miao, and Yunting
Sun. Special thanks to Emil Martayan for helping add baseline_weights support.