pyvene-interventions

SKILL.md

pyvene: Causal Interventions for Neural Networks

pyvene is Stanford NLP's library for performing causal interventions on PyTorch models. It provides a declarative, dict-based framework for activation patching, causal tracing, and interchange intervention training - making intervention experiments reproducible and shareable.

GitHub: stanfordnlp/pyvene (840+ stars)
Paper: pyvene: A Library for Understanding and Improving PyTorch Models via Interventions (NAACL 2024)

When to Use pyvene

Use pyvene when you need to:

Perform causal tracing (ROME-style localization)

Run activation patching experiments

Conduct interchange intervention training (IIT)

Test causal hypotheses about model components

Share/reproduce intervention experiments via HuggingFace

Work with any PyTorch architecture (not just transformers)