attribution patching

**Attribution patching** is the **approximate patching method that estimates intervention effects using gradient-based attribution rather than exhaustive full patches** - it accelerates causal screening over large component spaces. **What Is Attribution patching?** - **Definition**: Uses local linear approximations to predict effect of replacing activations. - **Speed Benefit**: Much faster than brute-force patching across many heads and positions. - **Use Case**: Good for ranking candidate components before detailed causal validation. - **Approximation Limit**: Accuracy depends on local linearity and may miss nonlinear interactions. **Why Attribution patching Matters** - **Scalability**: Enables broad interpretability scans on large models and long contexts. - **Prioritization**: Helps focus expensive full interventions on most promising targets. - **Workflow Efficiency**: Reduces compute cost in early mechanism discovery stages. - **Method Complement**: Pairs well with exact patching for confirmatory analysis. - **Caution**: Approximate rankings require validation before strong causal claims. **How It Is Used in Practice** - **Two-Stage Workflow**: Use attribution patching for triage, then exact patching for confirmation. - **Stability Checks**: Compare ranking consistency across prompts and metric definitions. - **Error Analysis**: Audit cases where approximate and exact effects disagree. Attribution patching is **a compute-efficient screening tool for causal interpretability workflows** - attribution patching adds speed and scale when paired with rigorous follow-up validation.