This is the implementation for the experiments in the paper Causal Abstractions of Neural Natural Language Inference Models.

See requirements.txt.

• intervention/ Basic infrastructure for defining computation graphs and performing interventions.
• compgraphs/ Computation graphs for Natural Language Inference causal models and neural models.
• causal_abstraction/ Interchange experiments and analysis.
• datasets/ Class definitions for datasets.
• modeling/ Neural models for NLI and training code.
• probing/ Probing experiments.
• experiment/ Utilities for launching experiments and automatically recording experiment results in databases.
• feature_importance/ Utilities for integrated gradients experiments.

Training models

• train_bert.py and train_lstm.py. Train one instance of a model.
• train_manager.py. Utilities for interfacing with the experiment module and managing grid search training.

Interchange experiments

• interchange.py. Run one set of an interchange experiment on a given causal model intermediate node, and all neural model locations for that node. Analyze the success rates of interventions.
• graph_analysis.py. Composes the graph linking the examples after interchange experiments and finds cliques.
• interchange_manager.py. Utilities for interfacing with the experiment module and run large batches of interchange experiments on a computing cluster.

Probing experiments

• probe.py
• probe_manager.py