论文标题
在随机分配下，对潜在结果的强大而有效的估计
Robust and Efficient Estimation of Potential Outcome Means under Random Assignment
论文作者
论文摘要
我们研究了随机实验的效率提高，以估算有两个以上治疗水平时使用回归调整（RA）的潜在结果载体的效率。我们表明，与使用子样本平均值相比，在每个分配级别估算每个分配水平的单独斜率的线性RA永远不会更糟。我们还表明，除了在不同的分配级别上线性投影中的斜率参数相同的情况下，除了在显而易见的情况下，RA对RA进行了单独的改进。我们进一步表征了尽管有条件平均功能的任意错误指定，但仍能保留潜在结果一致性的非线性RA方法。最后，我们应用这些回归调整技术来有效估计加利福尼亚州预防漏油计划的下限平均值意愿。
We study efficiency improvements in randomized experiments for estimating a vector of potential outcome means using regression adjustment (RA) when there are more than two treatment levels. We show that linear RA which estimates separate slopes for each assignment level is never worse, asymptotically, than using the subsample averages. We also show that separate RA improves over pooled RA except in the obvious case where slope parameters in the linear projections are identical across the different assignment levels. We further characterize the class of nonlinear RA methods that preserve consistency of the potential outcome means despite arbitrary misspecification of the conditional mean functions. Finally, we apply these regression adjustment techniques to efficiently estimate the lower bound mean willingness to pay for an oil spill prevention program in California.
