Professor Karla Diaz-Ordaz
PhD
Professor
of Biostatistics
LSHTM
Keppel Street
London
WC1E 7HT
United Kingdom
My primary methodological research area is causal machine learning motivated by high-dimensional electronic health records and genomics data.
My work on treatment effect heterogeneity and optimal treatment regimes is funded through a Wellcome Trust-Royal Society Sir Henry Dale Fellowship (2020-2025).
I am also co-lead in a collaborative research project "Developping statistical machine learning methods for Clinical Trials" based at the Alan Turing Institute.
Previously, I held a Wellcome Trust-LSHTM Institutional Support Fellowship (2018-2019), an MRC Career Development Award in Biostatistics (2014-2018) and an NIHR Methods Fellowship (2009-2012).
In 2017, I was a visiting scholar to Prof. Mark van der Laan's Computational Biology and Causality group at the School of Public Health, University of California, Berkeley.
I have a PhD in Mathematics from Imperial College London and a MSc in Medical Statistics from the London School of Hygiene and Tropical Medicine.
PhD students:
Oliver Hines (2018- ): MRC London Intercollegiate Doctoral scholarship studying double-robust methods with machine learning in high-dimensional data, with applications to cardio-genetics mediation (joint with Prof Stijn Vansteelandt)
Matt Pryce (2021 - present): MRC London Intercollegiate Doctoral scholarship studying double-robust methods with machine learning for survival analysis in high dimensional settings.
Rebecca Xu (2021- present) : ESRC Doctoral Scholarship studying extensions to target trial emulation for group policy evaluation, in collaboration with the Health Foundation.
Former PhD students:
Dr Schadrac Agbla (awarded 2019): Instrumental Variable methods for adjusting for nonadherence in cluster randomised trials (joint with Prof Bianca DeStavola).
Dr Anower Hossain (awarded 2017): Missing data methodology for cluster randomised trials (joint with Dr Jonathan Bartlett).
Affiliations
Teaching
I am the co-organiser of the School's short course Causal Inference in Epidemiology: Recent Methodological Developments
I am also the teach on the Advanced Statistical Methods (Causal Inference) sub-module in the MSc in Medical Statistics.
I am also a co-organiser of the Machine Learning Module in the MSc in Health Data Science.
Research
My current work involves doubly-robust estimators paired with the machine learning estimation of the nuisance parameters (e.g. Super Learner estimation). Examples of these are Targeted Minimum Loss estimators (TMLE) and g-estimators with machine learning. These methods are very promising to study causal effects using big data. This is in collaboration with Prof Stijn Vansteelandt (U Ghent).
I am also a co-Principal investigator (together with Prof Chris Holmes) on a project scoping the uses of machine learning in clinical trials, at the Alan Turing Institute.
Previous work involved developing methods for estimating causal treatment effect when there is departures from protocol in a randomised trial (i.e. non-compliance and missing data) using Multiple Imputation (collaboration with Prof James Carpenter). I have also worked in extending methods for cost-effectiveness analysis, accounting for the bivariate nature of the endpoints (with Prof Richard Grieve).
Some of my methodological work code can be found in my GitHub page.I am a member of the Centre for Statistical Methodology, and one of the co-ordinators of the missing data and causal inference themes.
