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.

Previous research found that less accommodating caseworkers are more successful in placing unemployed workers into employment. This paper explores the causal mechanisms behind this result using semi-parametric mediation analysis. Analyzing rich linked job seeker-caseworker data for Switzerland, we find that the positive employment effects of less accommodating caseworkers are not driven by a particularly effective mix of labor market programs but, rather, by other dimensions of the counseling process, possibly including threats of sanctions and pressure to accept jobs.

We derive testable implications of instrument validity in just identified treatment effect models with endogeneity and consider several tests. The identifying assumptions of the local average treatment effect allow us to both point identify and bound the mean potential outcomes of the always takers under treatment and the never takers under nontreatment. The point-identified means must lie within their respective bounds, which provides us with four testable inequality moment constraints. Finally, we adapt our testing framework to the identification of distributional features. A brief simulation study and an application to labor market data are also provided.