随机缺失机制下因果效应估计方法的比较

The Comparison of Causal Effect Estimation Methods at Missing at Random

在结局变量有缺失条件下,本文提出四种因果效应的方法:倾向值加权法(PW),改进的倾向值加权法(IPW),广义倾向值加权法(AIPW),回归估计法(REG),给出了其无偏性、一致性的证明.同时证明了AIPW方法的双重稳健性.通过在不同缺失程度下模拟比较,说明了AIPW方法较其它三种方法更为准确、有效.最后利用四种估计方法对美国儿童和青少年福利调查的数据进行了因果效应分析,得出接受药物干预服务的儿童并没有比未接受药物滥用服务的孩子表现出更严重的行为问题.

When the dependent variable is missing at random, the paper first proposes the four causal effect es- timation methods： propensity scores weighted method （PW）, improved propensity score weighted method （IPW）, augmented propensity weighted estimator （AIPW）, regression estimator （REG） and proves the unbiasedness and consistency of the four methods. The paper also proves that AIPW method is double robustness. The four methods are compared when the missing ratio is in different level. It is illuminat- ed that AIPW is more precise and more efficient than other methods. Finally, the causal effect of the American academy of child and adolescent welfare survey data is estimated with the four methods and the results are reached that children accept drug intervention service show no more serious behavior problems than the children who don＇t accept drug abuse services.