ISOTONIC PROPENSITY SCORE MATCHING
提出一种基于等渗回归估计倾向得分的一对多匹配估计量，利用单调性假设解决现有匹配方法的效率、参数选择、稳健性和自助法有效性问题。
We propose a one-to-many matching estimator of the average treatment effect based on propensity scores estimated by isotonic regression. This approach is predicated on the assumption of monotonicity in the propensity score function, a condition that can be justified in many economic applications. We show that the nature of the isotonic estimator can help us to fix many problems of existing matching methods, including efficiency, choice of the number of matches, choice of tuning parameters, robustness to propensity score misspecification, and bootstrap validity. As a by-product, a uniformly consistent isotonic estimator is developed for our proposed matching method.