4 citations · 9 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
Chasing Shadows: How Implausible Assumptions Skew Our Understanding of Causal Estimands
Stijn Vansteelandt, Kelly Van Lancker
The ICH E9 (R1) addendum on estimands, coupled with recent advancements in causal inference, has prompted a shift towards using model-free treatment effect estimands that are more…
Causal machine learning methods and use of cross-fitting in settings with high-dimensional confounding
Susan Ellul, Stijn Vansteelandt, John B. Carlin +1
Observational epidemiological studies commonly seek to estimate the causal effect of an exposure on an outcome. Adjustment for potential confounding bias in modern studies is chall…
Automated, efficient and model-free inference for randomized clinical trials via data-driven covariate adjustment
Kelly Van Lancker, Iván Díaz, Stijn Vansteelandt
In 2023, the U.S. Food and Drug Administration issued guidance for adjustment of covariates in randomized clinical trials, emphasizing its role in enhancing precision and power thr…