Testing Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-Dimensional Data
提出一种Cramér-von Mises型检验，用于在无混淆假设下检验连续处理变量的平均潜在结果是否单调，采用双重去偏机器学习方法控制高维协变量，并应用于Job Corps项目评估。
Abstract We propose a Cramér-von Mises-type test for testing whether the mean potential outcome given a specific treatment level has a weakly monotonic relationship with the continuous treatment under unconfoundedness. To flexibly control for a possibly high-dimensional set of covariates, our test is based on a double debiased machine learning method. We show that our test controls asymptotic size and is consistent against any fixed alternative. We apply our test to evaluate the Job Corps program and reject a weakly negative relationship between the treatment (hours in academic and vocational training) and labor market performance among relatively low treatment values.