A FLEXIBLE NONPARAMETRIC TEST FOR CONDITIONAL INDEPENDENCE
提出一种易于实现且具有一致性和局部幂的条件独立性非参数检验，通过估计受限与不受限概率测度间的拓扑距离构造检验统计量，并采用核平滑和条件模拟方法，在蒙特卡洛实验中表现良好，应用于教育回报估计中的无混淆性假设检验。
This paper proposes a nonparametric test for conditional independence that is easy to implement, yet powerful in the sense that it is consistent and achieves n −1/2 local power. The test statistic is based on an estimator of the topological “distance” between restricted and unrestricted probability measures corresponding to conditional independence or its absence. The distance is evaluated using a family of Generically Comprehensively Revealing (GCR) functions, such as the exponential or logistic functions, which are indexed by nuisance parameters. The use of GCR functions makes the test able to detect any deviation from the null. We use a kernel smoothing method when estimating the distance. An integrated conditional moment (ICM) test statistic based on these estimates is obtained by integrating out the nuisance parameters. We simulate the critical values using a conditional simulation approach. Monte Carlo experiments show that the test performs well in finite samples. As an application, we test an implication of the key assumption of unconfoundedness in the context of estimating the returns to schooling.