Moser, AndréAndréMoserPuhan, Milo AMilo APuhanZwahlen, MarcelMarcelZwahlen0000-0002-6772-63462024-09-022024-09-022020-03https://boris-portal.unibe.ch/handle/20.500.12422/34153In a recent issue of the American Journal of Public Health, Herna´n and other colleagues strongly plea for causal thinking in scientific research where the research question investigates consequences of decisions and interventions (Ahern 2018; Begg and March 2018; Chiolero 2018; Glymour and Hamad 2018; Herna´n 2018a, b; Jones and Schooling 2018). Herna´n argues that causal reasoning improves quality of observational research; however, the causal terminology is often loomed by the ‘association is not causation’ argument and is viewed with skepticism (Herna´n 2018b). Health services research (HSR) supports decision making by investigating the effect of complex ‘interventions’ or ‘policies’ on different healthcare system outcomes (Glass et al. 2013). Thus, some of the research questions in HSR are inherently causal. Surprisingly, there is no consensus on how to integrate causal inference into tasks of HSR (Dowd 2011; O’Malley 2011; Pearl 2011; Herna´n et al. 2019). Typically, tasks in data science are classified into ‘description’, ‘modeling’ and ‘causal inference’ (Herna´n et al. 2019). In the present Hints and Kinks, we explain why a solidly principled causal inference framework should be integrated into the tasks of HSR.en600 - Technology::610 - Medicine & health300 - Social sciences, sociology & anthropology::360 - Social problems & social servicesarticle10.7892/boris.1406883205208610.1007/s00038-020-01333-2