Distributional Balancing for Causal Inference: A Unified Framework via Characteristic Function Distance
因果推断中的分布平衡:基于特征函数距离的统一框架
Weighting methods are essential tools for estimating causal effects in observational studies, with the goal of balancing pre-treatment covariates across treatment groups. Traditional approaches pursue this objective indirectly, for example, via inverse propensity score weighting or by matching a finite number of covariate moments, and therefore do not guarantee balance of the full joint covariate distributions. Recently, distributional balancing methods have emerged as…