论文标题
最佳选择KNN算法中控制单元数量以估计平均治疗效果
Optimal selection of the number of control units in kNN algorithm to estimate average treatment effects
论文作者
论文摘要
我们提出了一种简单的方法，以最佳选择k最近的邻居（KNN）算法的控制单元数量，以最大程度地减少平均治疗效果的平方误差。我们的方法是非参数，其中使用具有偏置校正的渐近结果计算了治疗效果的置信区间。仿真练习表明，我们的方法会出现相对小的平方错误，并且置信区间长度和I型错误之间的平衡。我们分析了401（k）计划对累积的净金融资产计划的治疗（ATET）的平均治疗效果，从而确认了对净资产的数量和积极概率的重大影响。与使用k = 1的常见实践相比，我们的最佳K选择会产生明显的狭窄ATET置信区间。
We propose a simple approach to optimally select the number of control units in k nearest neighbors (kNN) algorithm focusing in minimizing the mean squared error for the average treatment effects. Our approach is non-parametric where confidence intervals for the treatment effects were calculated using asymptotic results with bias correction. Simulation exercises show that our approach gets relative small mean squared errors, and a balance between confidence intervals length and type I error. We analyzed the average treatment effects on treated (ATET) of participation in 401(k) plans on accumulated net financial assets confirming significant effects on amount and positive probability of net asset. Our optimal k selection produces significant narrower ATET confidence intervals compared with common practice of using k=1.
