6 citations · 21 across the 12 of their papers we have counts for
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cs.CL2022★ 6 cited
Causal Proxy Models for Concept-Based Model Explanations
Zhengxuan Wu, Karel D'Oosterlinck, Atticus Geiger +2
Explainability methods for NLP systems encounter a version of the fundamental problem of causal inference: for a given ground-truth input text, we never truly observe the counterfa…
cs.CL2022★ 6 cited
CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior
Eldar David Abraham, Karel D'Oosterlinck, Amir Feder +5
The increasing size and complexity of modern ML systems has improved their predictive capabilities but made their behavior harder to explain. Many techniques for model explanation…