A Framework for Covariate Balance using Bregman Distances

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.

References in corpus (6)

• Balancing Covariates via Propensity Score Weighting
• For objective causal inference, design trumps analysis
• Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data
• Entropy balancing is doubly robust
• Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations
• Minimal Dispersion Approximately Balancing Weights: Asymptotic Properties and Practical Considerations

Cited by in corpus (2)

• Improving trial generalizability using observational studies
• A Calibration Approach to Transportability and Data-Fusion with Observational Data