Causal inference from observational data.
Randomized controlled trials have long been considered the 'gold standard' for causal inference in clinical research. In the absence of randomized experiments, identification of reliable intervention points to improve oral health is often perceived as a challenge. But other fields of science, such as social science, have always been challenged by ethical constraints to conducting randomized controlled trials. Methods have been established to make causal inference using observational data, and these methods are becoming increasingly relevant in clinical medicine, health policy and public health research. This study provides an overview of state-of-the-art methods specifically designed for causal inference in observational data, including difference-in-differences (DiD) analyses, instrumental variables (IV), regression discontinuity designs (RDD) and fixed-effects panel data analysis. The described methods may be particularly useful in dental research, not least because of the increasing availability of routinely collected administrative data and electronic health records ('big data').
Department of Conservative Dentistry, Translational Health Economics Group (THE Group), Heidelberg University, Heidelberg, Germany.|Munich Center for the Economics of Aging, Max-Planck-Institute for Social Law and Social Policy, Munich, Germany.
Stefan
S
Listl
Schumpeter School of Business and Economics, University of Wuppertal, Wuppertal, Germany.
Hendrik
H
Jürges
Department of Epidemiology and Public Health, University College London, London, United Kingdom.
Richard G
RG
Watt
D015984:Causality; D003627:Data Interpretation, Statistical; D018865:Dental Research / Q000191:economics / Q000941:ethics / Q000379:methods; D006801:Humans; D064887:Observational Studies as Topic; D012044:Regression Analysis
causality; economics; epidemiology; health policy; observational study; public health