2 citations · 4 across the 3 of their papers we have counts for
3 papers
stat.ML2023★ 2 cited
Variable importance for causal forests: breaking down the heterogeneity of treatment effects
Clément Bénard, Julie Josse
Causal random forests provide efficient estimates of heterogeneous treatment effects. However, forest algorithms are also well-known for their black-box nature, and therefore, do n…
stat.ME2023★ 1 cited
Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data
Pan Zhao, Julie Josse, Shu Yang
An individualized treatment regime (ITR) is a decision rule that assigns treatments based on patients' characteristics. The value function of an ITR is the expected outcome in a co…
stat.ME2014★ 1 cited
Confidence Areas for Fixed-Effects PCA
Julie Josse, Stefan Wager, François Husson
PCA is often used to visualize data when the rows and the columns are both of interest. In such a setting there is a lack of inferential methods on the PCA output. We study the asy…