Covariate-balancing-propensity-score-based inference for linear models with missing responses

Based on covariate balancing propensity score (CBPS), improved estimators for the regression coefficients and population mean of linear models are obtained, when the responses are missing at random. It is proved that the proposed estimators are asymptotically normal.