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 a...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
25 April 2016
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| In: |
Community dentistry and oral epidemiology
Year: 2016, Volume: 44, Issue: 5, Pages: 409-415 |
| ISSN: | 1600-0528 |
| DOI: | 10.1111/cdoe.12231 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1111/cdoe.12231 Verlag, lizenzpflichtig, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1111/cdoe.12231 
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| Author Notes: | Stefan Listl, Hendrik Jürges and Richard G. Watt |
| Summary: | 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’). |
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| Item Description: | Gesehen am 27.05.2020 |
| Physical Description: | Online Resource |
| ISSN: | 1600-0528 |
| DOI: | 10.1111/cdoe.12231 |