Activation patching is the causal intervention method that replaces selected activations in one run with activations from another run to test influence on outputs - it is one of the most widely used tools in mechanistic interpretability.

What Is Activation patching?

• Definition: Patch operation swaps activations at chosen layer, position, and component granularity.
• Purpose: Measures whether a component carries task-relevant information for target behavior.
• Variants: Can patch attention head outputs, MLP outputs, residual stream slices, or neuron groups.
• Readout: Effect size is measured by changes in logits, probabilities, or task success metrics.

Why Activation patching Matters

• Causal Evidence: Directly tests necessity and sufficiency of internal signals.
• Circuit Discovery: Helps isolate components that form behavior-driving pathways.
• Debugging: Identifies where incorrect behavior first enters computation.
• Safety Analysis: Useful for tracing risky output generation routes.
• Method Versatility: Applies across many tasks and model architectures.

How It Is Used in Practice

• Baseline Design: Use paired clean and corrupted prompts with clear behavioral contrast.
• Granularity Sweep: Start broad then narrow to specific heads or features.
• Robustness: Repeat patch tests across multiple prompt templates to avoid spurious conclusions.

Activation patching is a foundational causal tool for transformer mechanism analysis - activation patching is most reliable when experiment design cleanly isolates the behavior under study.