Short and Simple Confidence Intervals When the Directions of Some Effects Are Known
提出了一种在控制变量系数等干扰参数符号已知时，针对感兴趣参数的适应性置信区间。该区间计算简单，在干扰参数较小时比标准区间更短，且在任何参数值下长度增加极小，适用于线性回归模型，并通过实地实验和蒙特卡洛研究验证了其性质。
Abstract We introduce adaptive confidence intervals on a parameter of interest in the presence of nuisance parameters, such as coefficients on control variables, with known signs. Our confidence intervals are trivial to compute and can provide significant length reductions relative to standard ones when the nuisance parameters are small. At the same time, they entail minimal length increases at any parameter values. We apply our confidence intervals to the linear regression model, prove their uniform validity, and illustrate their length properties in an empirical application to a factorial design field experiment and a Monte Carlo study calibrated to the empirical application.