论文标题
在随机实验中对回归调整的统一分析
A unified analysis of regression adjustment in randomized experiments
论文作者
论文摘要
回归调整通常在随机试验中广泛应用，因为它通常提高了治疗效应估计器的精度。但是，以前的工作表明，这并不总是正确的。为了进一步了解这一现象，我们对一类线性回归调整后的估计量的渐近方差进行了统一的比较。我们的分析基于具有异性误差的线性回归的经典理论，因此不假定假定的线性模型是正确的。对于完全随机的二元处理，我们提供了足够的条件，在这些条件下，保证某些回归调整后的估计量比其他估计量更有渐近。我们探索其他设置，例如一般治疗分配机制和广义线性模型，并发现不再发生方差优势现象。
Regression adjustment is broadly applied in randomized trials under the premise that it usually improves the precision of a treatment effect estimator. However, previous work has shown that this is not always true. To further understand this phenomenon, we develop a unified comparison of the asymptotic variance of a class of linear regression-adjusted estimators. Our analysis is based on the classical theory for linear regression with heteroscedastic errors and thus does not assume that the postulated linear model is correct. For a completely randomized binary treatment, we provide sufficient conditions under which some regression-adjusted estimators are guaranteed to be more asymptotically efficient than others. We explore other settings such as general treatment assignment mechanisms and generalized linear models, and find that the variance dominance phenomenon no longer occurs.
