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Poster
Causal Abstractions of Neural Networks
Atticus Geiger · Hanson Lu · Thomas Icard · Christopher Potts

Thu Dec 09 04:30 PM -- 06:00 PM (PST) @
in Poster Session 7 »

Structural analysis methods (e.g., probing and feature attribution) are increasingly important tools for neural network analysis. We propose a new structural analysis method grounded in a formal theory of causal abstraction that provides rich characterizations of model-internal representations and their roles in input/output behavior. In this method, neural representations are aligned with variables in interpretable causal models, and then interchange interventions are used to experimentally verify that the neural representations have the causal properties of their aligned variables. We apply this method in a case study to analyze neural models trained on Multiply Quantified Natural Language Inference (MQNLI) corpus, a highly complex NLI dataset that was constructed with a tree-structured natural logic causal model. We discover that a BERT-based model with state-of-the-art performance successfully realizes parts of the natural logic model’s causal structure, whereas a simpler baseline model fails to show any such structure, demonstrating that neural representations encode the compositional structure of MQNLI examples.

Keywords: Theory · Deep Learning · Representation Learning · Causality · Interpretability

Author Information

Atticus Geiger (Stanford University)
Hanson Lu (Stanford University)
Thomas Icard (Stanford University)
Christopher Potts (Stanford University)

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