Causal inference in health services research: concepts, methods and application perspectives.

Health services research faces the challenge of providing sound recommendations for action for the further development of health systems and care. The application of causal inference methods offers health services researchers an excellent opportunity to identify causal relationships under everyday conditions. The role of clinical trials with a classic randomised controlled trial (RCT) design is recognised as suitable for gaining insights that help establish causal inference, but other methodological approaches to generating evidence also play an important role in health services research.The discussion paper presents key concepts and assumptions of causal inference and highlights their relevance for health services research. The paper makes it clear that in order to fulfil the assumptions, it is necessary to integrate theory, contextual knowledge, understanding of mechanisms and formal concepts, such as directed acyclic graphs (DAGs), into a suitable empirical study design. To this end, RCTs, quasi-experimental methods, causal machine learning, target trial emulation, in silico trials and the mixed-methods approach of integrated inference are presented and discussed in terms of their applicability in health services research and their internal and external validity.All of the approaches presented here can contribute to the estimation of causal effects when used in a targeted manner and in accordance with the central assumptions. Their suitability depends largely on the research question, data quality, theoretical modelling and contextual knowledge. The combination of complementary designs and high quality data sources can increase the robustness of causal conclusions.Causal inference in health services research is not only a methodological procedure, but an integrative process that systematically combines theory, methodology and contextual knowledge. By consistently linking these aspects, health services research can generate differentiated and actionable insights that go beyond correlative analyses and enable an understanding of the mechanisms of causal processes. This can lead, for example, to evidence-supported recommendations that critically examine the often non-evidence-based status quo and reliably evaluate the benefits of new models.