# A Framework for Covariate Balance using Bregman Distances

## TL;DR

This paper introduces a new framework for covariate balancing in observational studies using Bregman distances, providing a flexible and theoretically grounded approach that aligns with existing methods.

## Contribution

It proposes a convex optimization framework for covariate balancing weights based on Bregman distances, unifying and generalizing existing covariate balancing techniques.

## Key findings

- The framework produces weights identical to existing covariate balancing methods.
- Numerical studies demonstrate the equivalence and effectiveness of the proposed approach.

## Abstract

A common goal in observational research is to estimate marginal causal effects in the presence of confounding variables. One solution to this problem is to use the covariate distribution to weight the outcomes such that the data appear randomized. The propensity score is a natural quantity that arises in this setting. Propensity score weights have desirable asymptotic properties, but they often fail to adequately balance covariate data in finite samples. Empirical covariate balancing methods pose as an appealing alternative by exactly balancing the sample moments of the covariate distribution. With this objective in mind, we propose a framework for estimating balancing weights by solving a constrained convex program where the criterion function to be optimized is a Bregman distance. We then show that the different distances in this class render identical weights to those of other covariate balancing methods. A series of numerical studies are presented to demonstrate these similarities.