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...

Uncovering the heterogeneity of causal effects of policies and business decisions at various levels of granularity provides substantial value to decision makers. This paper develops new estimation and inference procedures for multiple treatment models in a selection-on-observables frame-work by...

In recent years, microeconometrics experienced the "credibility revolution", culminating in the 2021 Nobel prices for David Card, Josh Angrist, and Guido Imbens. This "revolution" in how to do empirical work led to more reliable empirical knowledge of the causal effects of certain public...