论文标题
关于平均治疗效果的回归调整后的估算值
On regression-adjusted imputation estimators of the average treatment effect
论文作者
论文摘要
使用估计的回归函数推出缺失的潜在结果是估计因果效应的自然思想。在文献中，据信估算和回归调整结合的估计器可与增强的反概率加权相媲美。因此，人们长期以来一直猜想，这种估计量在避免直接构建权重的同时也是双重的（Imbens，2004； Stuart，2010）。本文概括了作者的早期结果（Lin等，2021），正式化了这一猜想，表明大量的回归调整后的插补方法确实是双重鲁棒的，可以使估计平均治疗效果。此外，只要正确指定了密度和回归模型，它们就可以在半绘制效率上有效。我们理论所涵盖的插入方法的示例包括内核匹配，（加权）最近的邻居匹配，局部线性匹配和（诚实的）随机森林。
Imputing missing potential outcomes using an estimated regression function is a natural idea for estimating causal effects. In the literature, estimators that combine imputation and regression adjustments are believed to be comparable to augmented inverse probability weighting. Accordingly, people for a long time conjectured that such estimators, while avoiding directly constructing the weights, are also doubly robust (Imbens, 2004; Stuart, 2010). Generalizing an earlier result of the authors (Lin et al., 2021), this paper formalizes this conjecture, showing that a large class of regression-adjusted imputation methods are indeed doubly robust for estimating the average treatment effect. In addition, they are provably semiparametrically efficient as long as both the density and regression models are correctly specified. Notable examples of imputation methods covered by our theory include kernel matching, (weighted) nearest neighbor matching, local linear matching, and (honest) random forests.
