5 citations · 8 across the 12 of their papers we have counts for
4 papers · 1 filter
Unsupervised Causal Abstractions Discovery
Théo Saulus, Simon Lacoste-Julien, Dhanya Sridhar
Causal abstractions formalize when a high-level structural causal model (SCM) captures the interventional behavior of a lower-level SCM. Existing applications of this notion largel…
The Role of Causal Features in Strategic Classification for Robustness and Alignment
Antonio Gois, Sophia Gunluk, Nir Rosenfeld +3
In strategic classification, an institution (e.g., a bank) anticipates adaptation from users who change their features to increase utility in a classification task (e.g., loan repa…
Causality is Key for Interpretability Claims to Generalise
Shruti Joshi, Aaron Mueller, David Klindt +3
Interpretability research on large language models (LLMs) has yielded important insights into model behaviour, yet recurring pitfalls persist: findings that do not generalise, and…
Who Guards the Guardians? The Challenges of Evaluating Identifiability of Learned Representations
Shruti Joshi, Théo Saulus, Wieland Brendel +3
Identifiability in representation learning is commonly evaluated using standard metrics (e.g., MCC, DCI, R^2) on synthetic benchmarks with known ground-truth factors. These metrics…