# A Python Library For Empirical Calibration

## TL;DR

This paper introduces a Python library called EC that efficiently computes empirical calibration weights to correct data biases in survey sampling and observational studies, offering a flexible and robust tool for statistical bias correction.

## Contribution

The paper presents a new Python library EC for empirical calibration, formulated as convex optimization, with enhanced efficiency, robustness, and usability features compared to existing software.

## Key findings

- EC is more efficient than existing software.
- EC supports various optimization objectives and inexact calibration.
- Demonstrated effectiveness on simulated and real-world data.

## Abstract

Dealing with biased data samples is a common task across many statistical fields. In survey sampling, bias often occurs due to unrepresentative samples. In causal studies with observational data, the treated versus 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 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 usability. We demonstrate its usage across various experiments with both simulated and real-world data.