EFFICIENCY IN ESTIMATION UNDER MONOTONIC ATTRITION
研究了在单调损耗（即离开多期研究的个体不再返回）且数据随机缺失的假设下，如何高效估计由反事实分布矩条件定义的参数，并提出了一个双重稳健估计量，模拟和实证中表现优异。
Attrition is monotonic when agents leaving multi-period studies do not return. Under a general missing at random (MAR) assumption, we study efficiency in estimation of parameters defined by moment restrictions on the distributions of the counterfactuals that were unrealized due to monotonic attrition. We discuss novel issues related to overidentification, usability of sample units, and the information content of various MAR assumptions for estimation of such parameters. We propose a standard doubly robust estimator for these parameters by equating to zero the sample analog of their respective efficient influence functions. Our proposed estimator performs well and vastly outperforms other estimators in our simulation experiment and empirical illustration.