Abstract:
Covariate balance is crucial for an unconfounded descriptive or causal comparison. However, lack of balance is common in observational studies. This article focuses on weighting strategies for balancing covariates. We define a general class of weights---the balancing weights---that balance the weighted distributions of the covariates between treatment groups.  These weights are closely related to the propensity score, and are flexible enough to accommodate different target populations.  This framework unifies existing weighting methods, and includes commonly used weights such as the inverse-probability weights as special cases. General large-sample results on nonparametric estimation based on these weights are derived.

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