Dr Karla Diaz-Ordaz
My primary methodological research area is causal inference with machine learning motivated by high-dimensional electronic health records and genomics data.
This work is currently funded through a Wellcome Trust-LSHTM Fellowship (2018-2019). I have previously held an MRC Career Development Award in Biostatistics (2014-2018) and an NIHR Methods Fellowship (2009-2012).
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.
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 currently have two 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, and
Schadrac Agbla (2015- ): ESRC Quantitative methods scholarship, studying Instrumental Variable methods for adjusting for nonadherence in cluster randomised trials.
Former PhD students worked on missing data methodology for cluster randomised trials (Dr Anower Hossain, awarded 2017) and measurement error (Dr Christen Gray, awarded 2018).
I am the co-organiser of the School's short courses Statistical Analysis with Missing Data using Multiple Imputation and Inverse Probability Weighting and Causal Inference in Epidemiology: Recent Methodological Developments
I am also the organiser of Advanced Statistical Methods (Causal Inference) sub-module in the MSc in Medical Statistics.
My current work involves doubly-robust estimators, in particular, those that can be paired with the use of ensemble machine learning methods (e.g. Super Learner). Examples of these are Targeted Minimum Loss estimators (TMLE). These methods are very promising to study causal effects using big data. This is in collaboration with Prof Stijn Vansteelandt and Rhian Daniel (Cardiff).
I am also working in 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. This work is in collaboration with Prof James Carpenter.I have also worked in extending methods for cost-effectiveness analysis, accounting for the bivariate nature of the endpoints. 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.