2 papers
stat.ME2026
Targeted learning of heterogeneous treatment effect curves for right censored or left truncated time-to-event data
Matthew Pryce, Karla Diaz-Ordaz, Ruth H. Keogh +1
In recent years, there has been growing interest in causal machine learning estimators for quantifying subject-specific effects of a binary treatment on time-to-event outcomes. Est…
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…