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 presents novel evidence for the prevalence of deviations from rational behavior in human decision making – and for the corresponding causes and consequences. The analysis is based on move-by-move data from chess tournaments and an identification strategy that compares behavior of...

This paper presents a novel approach to analyze human decision-making that involves comparing the behavior of professional chess players relative to a computational benchmark of cognitively bounded rationality. This benchmark is constructed using algorithms of modern chess engines and allows...

This paper presents novel evidence for the prevalence of deviations from rational behavior in human decision making – and for the corresponding causes and consequences. The analysis is based on move-by-move data from chess tournaments and an identification strategy that compares behavior of...

Ineffective fundraising lowers the resources charities can use for goods provision. We combine a field experiment and a causal machine-learning approach to increase a charity’s fundraising effectiveness. The approach optimally targets fundraising to individuals whose expected donations exceed...

Ineffective fundraising lowers the resources charities can use for goods provision. We combine a field experiment and a causal machine-learning approach to increase a charity's fundraising effectiveness. The approach optimally targets fundraising to individuals whose expected donations exceed...