Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data
模型误设与高维数据下倾向得分的正则化校准估计
Summary Propensity scores are widely used with inverse probability weighting to estimate treatment effects in observational studies. We study calibrated estimation as an alternative to maximum likelihood estimation for fitting logistic propensity score models. We show that, with possible model misspecification, minimizing the expected calibration loss underlying the calibrated estimators involves reducing both the expected likelihood loss and a measure of relative errors…