Minimum Wasserstein distance estimator under covariate shift: closed-form, super-efficiency and irregularity
协变量漂移下的最小 Wasserstein 距离估计:闭式解、超效率与非正则性
Covariate shift arises when covariate distributions differ between source and target populations while the conditional distribution of the response remains invariant, and it underlies problems in missing data and causal inference. We propose a minimum Wasserstein distance estimation framework for inference under covariate shift that avoids explicit modeling of outcome regressions or importance weights. The resulting W-estimator admits a closed-form expression and is…