2 citations · 3 across the 3 of their papers we have counts for
3 papers
Is merging worth it? Securely evaluating the information gain for causal dataset acquisition
Jake Fawkes, Lucile Ter-Minassian, Desi Ivanova +2
Merging datasets across institutions is a lengthy and costly procedure, especially when it involves private information. Data hosts may therefore want to prospectively gauge which…
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…
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…