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methods for causal inference using large-scale observational healthcare data. The project will address fundamental methodological challenges in estimating causal treatment effects from longitudinal
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someone with strong quantitative skills and an interest in using data to answer policy relevant research questions. The successful applicant will work with large administrative and survey datasets and apply
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Health Data Sciences and Informatics (OHDSI, https://ohdsi-europe.org ) initiative, dedicated to bring out the full value of observational health data through the OMOP Common Data Model and large-scale
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have experience with or are eager to work with large databases and database management software. You have starter skills in statistical, econometric, or other quantitative data analysis, and some
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. Analysis of this data will employ both qualitative and quantitative methods. Work on WP-2 will suit someone with an interest and aptitude for coding administrative data using large language models. It will
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faculty and a large network of research institutions and facilities. The MGSE does not charge tuition fees for attending the doctoral programme. As the umbrella organisation for the department's graduate
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Experience with firm level econometric analyses or with processing of large firm level datasets will be an advantage, but it is more important that you have experience with software tools such as Stata, SAS, R