I investigate causal machine learning (CML) methods to estimate effect heterogeneity by means of conditional average treatment effects (CATEs). In particular, I study whether the estimated effect heterogeneity can provide evidence for the theoretical labour supply predictions of Connecticut's...

Recent studies have proposed causal machine learning (CML) methods to estimate conditional average treatment effects (CATEs). In this study, I investigate whether CML methods add value compared to conventional CATE estimators by re-evaluating Connecticut's Jobs First welfare experiment. This...

This paper evaluates the effect of a voucher award system for assignment into vocational training on the employment outcomes of unemployed voucher recipients in Germany, along with the causal mechanisms through which it operates. It assesses the direct effect of voucher assignment net of actual...

This paper estimates the labor market effects of being awarded with a training voucher using an instrumental variable approach. In Germany all public sponsored further training programs are allocated through vouchers and the system, we study here, thus represents a major case of the use of...

The objective of providing vocational training for the unemployed is to increase their chances of re-employment and human capital accumulation. In comparison to mandatory course assignment by case workers, the awarding of vouchers increases recipients’ freedom to choose between different...

This paper assesses the performance of common estimators adjusting for differences in covariates, such as matching and regression, when faced with so-called common support problems. It also shows how different procedures suggested in the literature affect the properties of such estimators. Based...

The disclosure of the VW emission manipulation scandal caused a quasi-experimental market shock in the observable quality of VW diesel vehicles. We consider a classical model for adverse selection and sorting to derive an empirically testable hypothesis about the impact of observable quality on...

We investigate the finite sample performance of causal machine learning estimators for heterogeneous causal effects at different aggregation levels. We employ an Empirical Monte Carlo Study that relies on arguably realistic data generation processes (DGPs) based on actual data. We consider 24...