2 citations · 2 across the 5 of their papers we have counts for
5 papers
Targeted Highly Adaptive Lasso Minimum Loss Estimation of Target Functions
Vanessa Rodriguez, Karla Diaz-Ordaz, Brieuc Lehmann +1
We propose a Targeted Highly Adaptive Lasso for estimation of non-pathwise differentiable functional parameters such as the dose-response curve (DRC) for continuous exposure. We as…
The super learner for time-to-event outcomes: A tutorial
Ruth H. Keogh, Karla Diaz-Ordaz, Nan van Geloven +2
Estimating risks or survival probabilities conditional on individual characteristics based on censored time-to-event data is a commonly faced task. This may be for the purpose of d…
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data
Matthew Pryce, Karla Diaz-Ordaz, Ruth H. Keogh +1
When estimating heterogeneous treatment effects, missing outcome data can complicate treatment effect estimation, causing certain subgroups of the population to be poorly represent…
Causal-ICM: A Data Fusion Framework For Heterogeneous Treatment Effect Estimation With Multi-Task Gaussian Processes
Evangelos Dimitriou, Edwin Fong, Jens Magelund Tarp +2
Bridging the gap between internal and external validity is crucial for heterogeneous treatment effect estimation. Randomised controlled trials (RCTs), favoured for their internal v…
Parameterising the effect of a continuous treatment using average derivative effects
Oliver J. Hines, Karla Diaz-Ordaz, Stijn Vansteelandt
The average treatment effect (ATE) is commonly used to quantify the main effect of a binary treatment on an outcome. Extensions to continuous treatments are usually based on the do…