Frequently in experiments there is not only variance in the reaction of participants to treatment. The heterogeneity is patterned: discernible types of participants react differently. In principle, a finite mixture model is well suited to simultaneously estimate the probability that a given...

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...

We investigate heterogeneity in patterns of preferences for health insurance features using health insurance choice data from a controlled laboratory experiment. Within the experiment, participants make consecutive insurance choices based on choice sets that vary in composition and size. We keep...

In 2006, the Netherlands commenced market based reforms in its health care system. The reforms included selective contracting of health care providers by health insurers. This paper focuses on how health insurers may increase their market share on the health insurance market through selective...