Robust and Efficient Estimation of Potential Outcome Means Under Random Assignment
研究了随机实验中超过两种处理水平时，通过回归调整提高潜在结果均值估计效率的方法，证明单独回归调整优于混合回归，并应用于加州石油泄漏预防项目的支付意愿估计。
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.