论文标题
导航加权以改善缺失数据问题和因果推断的反概率加权
Navigated Weighting to Improve Inverse Probability Weighting for Missing Data Problems and Causal Inference
论文作者
论文摘要
逆概率加权（IPW）广泛用于解决包括因果推理在内的缺失数据问题，但由于倾向得分模型错误指定，可能会遭受较大的差异和偏见。为了解决这些问题，我提出了一种称为导航加权（NAWT）的估计方法，该方法利用了适合特定预先指定的感兴趣参数的估计方程（例如，对治疗的平均处理效果）。由于这些预先指定的参数确定了每个单元与倾向得分的相对重要性，因此NAWT在倾向得分估计中优先考虑重要单元，以提高效率和鲁棒性，以指定模型错误指定。我研究了其大样本特性，并通过模拟研究和经验例子证明了其有限的样品改进。将开发了实现NAWT的R软件包Nawtilus，并从综合的R档案网络（http://cran.r-project.org/package=nawtilus）中获得。
The inverse probability weighting (IPW) is broadly utilized to address missing data problems including causal inference but may suffer from large variances and biases due to propensity score model misspecification. To solve these problems, I propose an estimation method called the navigated weighting (NAWT), which utilizes estimating equations suitable for a specific pre-specified parameter of interest (e.g., the average treatment effects on the treated). Since these pre-specified parameters determine the relative importance of each unit as a function of propensity scores, the NAWT prioritizes important units in the propensity score estimation to improve efficiency and robustness to model misspecification. I investigate its large-sample properties and demonstrate its finite sample improvements through simulation studies and an empirical example. An R package nawtilus which implements the NAWT is developed and available from the Comprehensive R Archive Network (http://cran.r-project.org/package=nawtilus).
