Peter Tennant

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Associate Professor of Health Data Science

University of Leeds

United Kingdom

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About

Peter Tennant is an Associate Professor of Health Data Science at the University of Leeds, UK. His research primarily focuses on causal inference in health data, with recent publications addressing patient-reported outcome measures, causal directed acyclic graphs, and the implications of various modeling approaches in epidemiology. He has also contributed to the evaluation of COVID-19 impacts in England through counterfactual modeling studies.

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Alan Turing Fellow

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Articles (13)

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2025

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2025

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2025

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2022

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2022

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2022

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2022

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2022

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2022

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2021

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2020

Collaborators (16)

Geert Crombez

Professor of Health Psychology

Ghent University

BELGIUM

Peter Tennant

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Recent Grants

Articles (13)

How to develop causal directed acyclic graphs for observational health research: a scoping review

Depicting patient-reported outcome measures within directed acyclic graphs: practice and implications for causal reasoning

Depicting deterministic variables within directed acyclic graphs: an aid for identifying and interpreting causal effects involving derived variables and compositional data

Estimating the effects of lockdown timing on COVID-19 cases and deaths in England: A counterfactual modelling study

Causal and Associational Language in Observational Health Research: A Systematic Evaluation

Theory and performance of substitution models for estimating relative causal effects in nutritional epidemiology

Reply to WC Willett et al.

Adjustment for energy intake in nutritional research: a causal inference perspective

Analyses of ‘change scores’ do not estimate causal effects in observational data

Avoiding overadjustment bias in social epidemiology through appropriate covariate selection: a primer

Choice of Outcome in COVID-19 Studies and Implications for Policy: Mortality and Fatality.

Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations

Reflection on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning

Collaborators (16)

Geert Crombez

Nisreen Alwan

Eric Au

Georgia Tomova

Vinny Davies

Jelle Van Cauwenberg

Rebekah Brita Baglini

Sophie Pilleron

Saman Khalatbari-Soltani

Rachel A. Hoopsick

Mark Steven Gilthorpe

Talal Alshihayb

Matthew Franklin

Laurie Berrie

Onyebuchi Arah

Johannes Textor

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