Optimal Estimation When Researcher and Social Preferences Are Misaligned
将实验数据分析视为机制设计问题，考虑研究者根据自身偏好选择估计量，重点研究协变量调整中的偏差与均方误差权衡，提出固定偏差的估计量可作为样本分割程序，并讨论了允许有益规范搜索的次优估计量。
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.