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Localizing behavior to individual components of a language model is a central goal of mechanistic interpretability. However, scoring components one at a time misses context-dependent effects: a primary component can inhibit the activation of a backup, leading to issues with ranking components. Actual causality studies...
EMPIRIC, an agent that learns a residual world model: a physics engine extended with code for the missing mechanisms, that learns interpretable, reusable models, and solves more tasks with fewer environment interactions than all three baselines.
Probabilistic Causal Impact (PCI) builds on actual causality and on Pearl's notions of probability of necessity and sufficiency, but recasts the question of explainability as an estimation problem on a probabilistic causal model that is easily approximated via Monte Carlo.
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