2 citations · 2 across the 2 of their papers we have counts for
2 papers
stat.ME2024
Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation
Lucile Ter-Minassian, Liran Szlak, Ehud Karavani +2
Interpretability and transparency are essential for incorporating causal effect models from observational data into policy decision-making. They can provide trust for the model in…
cs.LG2024★ 2 cited
Explainable AI for survival analysis: a median-SHAP approach
Lucile Ter-Minassian, Sahra Ghalebikesabi, Karla Diaz-Ordaz +1
With the adoption of machine learning into routine clinical practice comes the need for Explainable AI methods tailored to medical applications. Shapley values have sparked wide in…