The Statistical Methods for Health Equity Series is a monthly online series co-hosted by the Data Science for Health Equity community, the Alan Turing Institute Health Equity Interest Group, and the Department of Statistical Science at University College London.

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For our first webinar of 2023, we are delighted to welcome Dr Joshua Loftus from London School of Economics and Political Sciences (LSE). His talk will be on "Using causality for model explanations and algorithmic fairness"

Joshua Loftus did his Ph.D. in Statistics at Stanford University and had been a Research Fellow at the Alan Turing Institute. Before joining LSE, he had also been appointed as an Assistant Professor at the New York University from 2017-2020. Dr Joshua Loftus has particular research interests in enhancing data science and machine learning techniques to reduce the impacts of bias, particularly biases associated with social harms and scientific reproducibility. This includes developing methods and software for statistical inference after model selection, and using causality to analyse the fairness and interpretability of algorithms in machine learning and artificial intelligence. More broadly, he is interested in high-dimensional statistics and causal inference, and in teaching theory, applications, and best practices in data science using the R statistical programming language.

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Please direct any questions about the webinar series to Dr Brieuc Lehmann at b.lehmann@ucl.ac.uk.