2 citations · 2 across the 5 of their papers we have counts for
Showing 2024Show all
2 papers · 1 filter
stat.ML2024
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…
stat.ME2024
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…