Quasi-likelihood estimation of average treatment effects based on model information

In this paper, the estimation of average treatment effects is considered when we have the model information of the conditional mean and conditional variance for the responses given the covariates. The quasi-likelihood method adapted to treatment effects data is developed to estimate the parameters in the conditional mean and conditional variance models. Based on the model information, we define three estimators by imputation, regression and inverse probability weighted methods. All the estimators are shown asymptotically normal. Our simulation results show that by using the model information, the substantial efficiency gains are obtained which are comparable with the existing estimators.

强可忽略处理分配下因果推断的结构回归模型

建立强可忽略处理分配条件下因果推断的结构回归模型，估计平均处理效应．在正态分布假 设下，总体参数的极大似然估计是渐近正态无偏估计．提出的方法可推广到具有测量误差的一般情 形．

因果推论与观察研究：「反事实模型」之思考

「反事实之因果模型」的出发点很简单：要确认D是Y的因，也必须反过来思考「那若没有D的话，Y会如何？」故因果效应的推论，应不只是建立在D和Y联袂发生的规律上，还要进一步比较「实际结果」（事实），和「可能但未发生的结果」（反事实）两者之差异。这固然不是因果推论唯一的定义与思维方式，但这个模型一方面能刺激「反事实」的逆向思考，另一方面却又能将观察不到的假想「反事实」操作化为控制（比较）组，逐渐发展成一套共通的因果推论架构，贯穿随机分派实验、准实验、自然实验以及非实验之观察研究。不但逻辑一贯，而且更能落实到具体可行的分析方法，对社会科学中无法或不易进行实验、但仍希望推论因果的观察研究，有相当大的启发。并澄清了传统实证分析方法中，过於偏重观察得到的因果规律等若干不够精确的观念，刺激了另一波方法论的反思。