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...

We systematically investigate the effect heterogeneity of job search programmes for unemployed workers. To investigate possibly heterogeneous employment effects, we combine non-experimental causal empirical models with Lasso-type estimators. The empirical analyses are based on rich...

This study analyses the effects of regular physical education at school on cognitive skills, non-cognitive skills, motor skills, physical activity, and health. It is based on a very informative data set, the German Motorik-Modul, and identifies the effect by using variation in the required...

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...

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...

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...

Binary treatments are often ex-post aggregates of multiple treatments or can be disaggregated into multiple treatment versions. Thus, effects can be heterogeneous due to either effect or treatment heterogeneity. We propose a decomposition method that uncovers masked heterogeneity, avoids...

This paper analyses the role of job changes in overcoming work hour constraints and the work hour mismatches resulting from these constraints (i.e., differences between actual and desired work hours). Building on previous findings that job change increases the flexibility of actual work hours,...

This paper consolidates recent methodological developments based on Double Machine Learning (DML) with a focus on program evaluation under unconfoundedness. DML based methods leverage flexible prediction methods to control for confounding in the estimation of (i) standard average effects, (ii)...

This paper analyses the role of job changes in overcoming work hour constraints and the work hour mismatches resulting from these constraints (i.e., differences between actual and desired work hours). Building on previous findings that job change increases the flexibility of actual work hours,...