TMCF workshop series: Prediction algorithms with a causal interpretation (online event)
Wednesday 06 May 2020
Time: 11:00 - 16:30
Bio
Peter Tennant is an Associate Professor of Health Data Science at the University of Leeds and Fellow of the Alan Turing Institute. Initially trained as an epidemiologist, his research is now primarily focussed on adapting and translating contemporary causal inference methods into health and social science research. He is also an experienced educator and renowned public speaker, who is regularly invited to speak to a diverse range of audiences. He leads the Causal Inference Interest Group within the Alan Turing Institute and the Introduction to Causal Inference Course for Health and Social Scientists.
Research interests
Peter’s research is primarily focussed on adapting and translating contemporary causal inference methods into health and social science research. This takes place across four overlapping themes:
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Methods and theory - increasing fundamental understanding of the analysis and interpretation of observational data using causal inference methods. Examples include his recent work on analyses of change scores and analyses of compositional data.
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Meta-science and guidelines – increasing the use and improving the quality of causal inference research. Examples include his reviews on the use of directed acyclic graphs and on the use of causal and association language.
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Applied domain implications - using causal inference theory to understand and explain methodological challenges in specific applied research domains. Examples include his recent work on adjustment for energy intake in nutritional research and the choice of outcomes in COVID-19 research.
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Applied causal research – using causal inference methods to estimate causal effects in health and social science settings. Examples include his recent work on the effect of gestational diabetes on the risk of stillbirth and the effect of fasting plasma glucose on the risk of large-for-gestational age
