92 citations · 110 across the 23 of their papers we have counts for
5 papers · 1 filter
Ensuring valid inference for hazard ratios after variable selection
Kelly Van Lancker, Oliver Dukes, Stijn Vansteelandt
The problem of how to best select variables for confounding adjustment forms one of the key challenges in the evaluation of exposure effects in observational studies, and has been…
Translating questions to estimands in randomized clinical trials with intercurrent events
Mats J. Stensrud, Oliver Dukes
Intercurrent (post-treatment) events occur frequently in randomized trials, and investigators often express interest in treatment effects that suitably take account of these events…
Proximal mediation analysis
Oliver Dukes, Ilya Shpitser, Eric J. Tchetgen Tchetgen
A common concern when trying to draw causal inferences from observational data is that the measured covariates are insufficiently rich to account for all sources of confounding. In…
On Doubly Robust Inference for Double Machine Learning in Semiparametric Regression
Oliver Dukes, Stijn Vansteelandt, David Whitney
Due to concerns about parametric model misspecification, there is interest in using machine learning to adjust for confounding when evaluating the causal effect of an exposure on a…
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…