中文摘要
计量经济学分析通常关注固定估计量的统计性质，而忽略了研究者的选择。本文转而将实验数据分析视为一个机制设计问题，承认研究者会在估计量之间进行选择，这种选择有时基于数据，往往取决于研究者自身的偏好。具体而言，本文关注协变量调整：协变量调整可以提高处理效应估计的精度，但当研究者进行设定搜索时，也为偏误的产生打开了大门。第一，本文证明，将无偏性作为平均处理效应估计的一项要求，可以使研究者的偏好与相对于真实值的均方误差最小化相一致，并且固定偏误可以在极小化极大意义上产生一种最优限制。第二，本文对具有固定偏误的处理效应估计量给出了构造性刻画，将其表征为样本分割程序。第三，本文讨论了次优估计量的实现问题，这类估计量为有益的设定搜索留出了空间。
Abstract
Econometric analysis typically focuses on the statistical properties of fixed estimators and ignores researcher choices. In this article, I instead approach the analysis of experimental data as a mechanism‐design problem that acknowledges that researchers choose between estimators, sometimes based on the data and often according to their own preferences. Specifically, I focus on covariate adjustments, which can increase the precision of a treatment‐effect estimate, but open the door to bias when researchers engage in specification searches. First, I establish that unbiasedness as a requirement on the estimation of the average treatment effect can align researchers' preferences with the minimization of the mean‐squared error relative to the truth, and that fixing the bias can yield an optimal restriction in a minimax sense. Second, I provide a constructive characterization of treatment‐effect estimators with fixed bias as sample‐splitting procedures. Third, I discuss the implementation of second‐best estimators that leave room for beneficial specification searches.