In social sciences and economics, causal inference traditionally focuses on assessing the impact of predefined treatments (or interventions) on predefined outcomes, such as the effect of education programs on earnings. Causal discovery, in contrast, aims to uncover causal relationships among...

This paper investigates the average effects of (firm-provided) workplace health promotion measures in form of the analysis of sickness absenteeism and health circles/courses on labour market out-comes of the firms’ employees. Exploiting linked employer-employee panel data that consist of rich...

We investigate the finite sample properties of a large number of estimators for the average treatment effect on the treated that are suitable when adjustment for observable covariates is required, like inverse probability weighting, kernel and other variants of matching, as well as different...

Identi cation in most sample selection models depends on the independence of the regressors and the error terms conditional on the selection probability. All quantile and mean functions are parallel in these models; this implies that quantile estimators cannot reveal any per assumption...

Using exceptionally rich linked administrative and survey information on German welfare recipients we investigate the health effects of transitions from welfare to employment and of assignments to welfare-to-work programmes. Applying semi-parametric propensity score matching estimators we find...

This paper investigates the average effects of (firm-provided) workplace health promotion measures in form of the analysis of sickness absenteeism and health circles/courses on labour market out-comes of the firms' employees. Exploiting linked employer-employee panel data that consist of rich...