Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or…
---
name: pyvene-interventions
description: Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Causal Intervention, pyvene, Activation Patching, Causal Tracing, Interpretability]
dependencies: [pyvene>=0.1.8, torch>=2.0.0, transformers>=4.30.0]
---
# 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](https://github.com/stanfordnlp/pyvene) (840+ stars)
**Paper**: [pyvene: A Library for Understanding and Improving PyTorch Models via Interventions](https://aclanthology.org/2024.naacl-demo.16) (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… load the full skill through Skill Me