3 citations · 9 across the 8 of their papers we have counts for
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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…
stat.ME2023
Challenges and Opportunities of Shapley values in a Clinical Context
Lucile Ter-Minassian, Sahra Ghalebikesabi, Karla Diaz-Ordaz +1
With the adoption of machine learning-based solutions in routine clinical practice, the need for reliable interpretability tools has become pressing. Shapley values provide local e…