92 citations · 108 across the 12 of their papers we have counts for
12 papers
Optimally weighted average derivative effects
Oliver Hines, Karla Diaz-Ordaz, Stijn Vansteelandt
Weighted average derivative effects (WADEs) are nonparametric estimands with uses in economics and causal inference. Debiased WADE estimators typically require learning the conditi…
Challenges and Opportunities of Shapley values in a Clinical Context
Lucile Ter-Minassian, Sahra Ghalebikesabi, Karla Diaz-Ordaz +1
With the adoption of machine learning-based solutions in routine clinical practice, the need for reliable interpretability tools has become pressing. Shapley values provide local e…
PWSHAP: A Path-Wise Explanation Model for Targeted Variables
Lucile Ter-Minassian, Oscar Clivio, Karla Diaz-Ordaz +2
Predictive black-box models can exhibit high accuracy but their opaque nature hinders their uptake in safety-critical deployment environments. Explanation methods (XAI) can provide…
Dynamic Updating of Clinical Survival Prediction Models in a Rapidly Changing Environment
Kamaryn Tanner, Ruth H. Keogh, Carol A. C. Coupland +2
Over time, the performance of clinical prediction models may deteriorate due to changes in clinical management, data quality, disease risk and/or patient mix. Such prediction model…
Variable importance measures for heterogeneous treatment effects
Oliver J. Hines, Karla Diaz-Ordaz, Stijn Vansteelandt
Motivated by applications in precision medicine and treatment effect heterogeneity, recent research has focused on estimating conditional average treatment effects (CATEs) using ma…
Demystifying statistical learning based on efficient influence functions
Oliver Hines, Oliver Dukes, Karla Diaz-Ordaz +1
Evaluation of treatment effects and more general estimands is typically achieved via parametric modelling, which is unsatisfactory since model misspecification is likely. Data-adap…
On Locality of Local Explanation Models
Sahra Ghalebikesabi, Lucile Ter-Minassian, Karla Diaz-Ordaz +1
Shapley values provide model agnostic feature attributions for model outcome at a particular instance by simulating feature absence under a global population distribution. The use…
How to estimate the association between change in a risk factor and a health outcome?
Michail Katsoulis, Alvina G Lai, Dimitra-Kleio Kipourou +8
Estimating the effect of a change in a particular risk factor and a chronic disease requires information on the risk factor from two time points; the enrolment and the first follow…
Robust Inference for Mediated Effects in Partially Linear Models
Oliver Hines, Stijn Vansteelandt, Karla Diaz-Ordaz
We consider mediated effects of an exposure, X on an outcome, Y, via a mediator, M, under no unmeasured confounding assumptions in the setting where models for the conditional expe…
Missing continuous outcomes under covariate dependent missingness in cluster randomised trials
Anower Hossain, Karla Diaz-Ordaz, Jonathan W. Bartlett
Attrition is a common occurrence in cluster randomised trials (CRTs) which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and in…
Instrumental variable approaches for estimating complier average causal effects on bivariate outcomes in randomised trials with non-compliance
Karla DiazOrdaz, Angelo Franchini, Richard Grieve
In Randomised Controlled Trials (RCT) with treatment non-compliance, instrumental variable approaches are used to estimate complier average causal effects. We extend these approach…
A comparison of multiple imputation methods for bivariate hierarchical outcomes
Karla Diaz-Ordaz, Michael G. Kenward, Manuel Gomes +1
Missing observations are common in cluster randomised trials. Approaches taken to handling such missing data include: complete case analysis, single-level multiple imputation that…