Arguing for Covariate Balance

Covariate balance tests in observational studies are frequently reduced to a single goal: minimizing sample covariate mean differences to (near) zero. However, a more expansive outlook on causal identification in general suggests that a credible design requires detailed contextual information about the empirical setting. I propose a framework for evaluating covariate balance that aligns with this more holistic perspective. It includes testing balance in multiple moments of covariate distributions and the estimation of balance statistic uncertainty via classical or Bayesian bootstrapping. This combination is more informative and intuitive than the conventional approach of assessing the statistical significance of covariate mean differences. Overall, the framework encourages analysts to argue positively for covariate balance rather than tenuously suggest the absence of imbalance.