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 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...

The third chapter estimates negative average effects of a Swiss job search programme for unemployed persons. Those who are send to the job search programme are significantly less likely to find a job quickly than comparable unemployed persons who do not participate in such a programme. Recent...

The second chapter documents mostly positive effects of more physical education on child development. Especially grades are substantially improved for students with more physical education. On the down side, behavioural problems of boys are increased at the same time. The empirical strategy...

This study investigates the dose-response effects of making music on youth development. Identification is based on the conditional independence assumption and estimation is implemented using a recent double machine learning estimator. The study proposes solutions to two highly practically...