Bio

Peter is a translational data scientist with a primary focus on understanding, adapting, and applying 'causal inference' methods to improve health and social science research. 

Peter is based at the Leeds Institute for Data Analytics, University of Leeds, where he teaches on their MSc in Health Data Analytics and Summer School in Causal Inference with Observational Data. He is also the University of Leeds representative on the UK Reproducibility Network.

Research interests

Peter's Turing Fellowship focuses on translating and adapting graphical methods for causal inference from their theoretical origins into 
the complex setting of applied health and social science research. 

This involves: 

1) Developing training and guidance 
on the application and reporting of causal diagrams and directed acyclic graphs, 

2) Adapting and developing notation to improve detection and understanding of common analytical errors, particularly arising from the analysis of deterministic variables and 

3) Demonstrating the utility and insights of 'causal inference' for resolving areas of debate and 
confusion in applied health and social science research.