Causal Tracing
Causal tracing is the interpretability workflow that maps where and when information causally influences model outputs across layers and positions - it reconstructs influence paths from input evidence to final predictions.
What Is Causal tracing?
- Definition: Combines targeted interventions with effect measurements along the computation graph.
- Temporal View: Tracks causal contribution as signal moves through layer depth.
- Spatial View: Localizes important token positions and component regions.
- Output: Produces influence maps that highlight key pathway bottlenecks.
Why Causal tracing Matters
- Failure Localization: Pinpoints where incorrect predictions become locked in.
- Circuit Validation: Confirms whether proposed circuits are actually behavior-critical.
- Safety Audits: Supports traceability for harmful or policy-violating outputs.
- Model Improvement: Guides targeted architecture or training interventions.
- Transparency: Provides interpretable causal story for complex model behavior.
How It Is Used in Practice
- Intervention Grid: Sweep layer and position combinations systematically for target behaviors.
- Effect Metrics: Use stable, behavior-relevant metrics rather than raw logit shifts alone.
- Cross-Validation: Check traced pathways across paraphrases and distractor variations.
Causal tracing is a high-value method for mapping causal information flow in transformers - causal tracing is strongest when intervention design and evaluation metrics are tightly aligned with task semantics.