We investigate the impact of the presence of university dropouts on the academic success of first-time students in universities of applied sciences. Our identification strategy relies on quasi-random variation in the proportion of dropouts. The estimated average zero effect of dropouts on first-time students’ success masks treatment heterogeneity and non-linearities. First, we find negative effects on the academic success of their new peers from dropouts who had re-enrolled in the same subject and, conversely, positive effects of dropouts changing subjects. Second, we use causal machine learning methods to find that the effects vary nonlinearly with different treatment intensities and prevailing treatment levels.