Feature Interpretation › Causal Validation: Steering and Ablation

Correlation between a feature and a concept is necessary but not sufficient for a valid interpretation. You also need to establish that the feature causally drives the behavior you have attributed to it. Two complementary causal methods are feature steering and feature ablation.

Feature steering involves artificially increasing a feature's activation and observing how the model's output changes. If a feature is tracking the concept "formal tone," then amplifying its activation should make the model's outputs more formal, regardless of what the input says. This kind of causal intervention is what distinguishes interpretability research from correlation analysis.

Operationally, you perform feature steering by computing the residual stream activation at a given layer in the normal forward pass, then adding a scaled multiple of the feature's decoder vector:

$$\mathbf{x}_{\text{steered}} = \mathbf{x} + \alpha \cdot \mathbf{d}_i$$

where:

• $\mathbf{x}$ is the original residual stream activation
• $\mathbf{d}_i$ is the decoder weight vector for feature $i$ (the direction in activation space the feature represents)
• $\alpha$ is the steering coefficient, which controls the strength of the intervention
• $\mathbf{x}_{\text{steered}}$ is the modified activation that will be passed to subsequent layers

The decoder vector is used for steering rather than the encoder vector because the decoder vector is the "concept direction" in residual stream space: it is the direction along which the model writes information associated with feature $i$. By adding $\alpha \mathbf{d}_i$ to the residual stream, you are injecting additional "evidence" for concept $i$ at that layer, as if the upstream circuit had activated the feature more strongly.

When steering succeeds in producing the expected behavior change, it provides strong evidence for the proposed interpretation. When it fails, it reveals either that the interpretation is wrong (the feature tracks a different concept) or that the feature is necessary but not sufficient for the behavior (other features must also be active for the behavior to manifest). Both outcomes are informative.

Steering experiments have produced some of the most vivid results in interpretability research. Anthropic's "Scaling Monosemanticity" paper demonstrated that steering on features associated with specific entities or concepts (such as a well-known person) could cause the model to produce text about that entity in a controlled way. More dramatically, steering on features associated with emotional states could shift the tone of model outputs along a spectrum from calm to agitated, giving direct evidence that these emotional features have a causal role in generation.

Feature ablation is the complementary operation to steering. Instead of amplifying a feature, you suppress it: set its activation to zero for a given input and measure the change in output. If a feature is necessary for a behavior, ablating it should cause the behavior to disappear or degrade.

Formally, ablation is applied by subtracting the feature's contribution from the residual stream:

$$\mathbf{x}_{\text{ablated}} = \mathbf{x} - a_i \cdot \mathbf{d}_i$$

where $a_i$ is the natural activation value that the feature achieves on the given input. This removes the feature's contribution exactly without affecting other features, as long as the SAE reconstruction is exact. In practice, SAEs have some reconstruction error, so ablation is approximate.

Ablation experiments reveal which features are necessary for a given behavior, while steering experiments reveal which features are sufficient. A feature that is both necessary (ablation kills the behavior) and sufficient (steering induces the behavior) has the strongest possible causal evidence for its proposed role. In practice, most features are either necessary or sufficient but not both, which is informative about the structure of the underlying circuit.

One important subtlety is that ablating a single feature rarely has a dramatic effect on a model's output. Most model behaviors are distributed across many features, so removing one is like removing a few bricks from a wall: the structure remains largely intact. This is consistent with good generalization: a model that relied on a single feature for a critical behavior would be fragile to noise in that feature. Meaningful ablation effects are typically found when you ablate entire groups of related features, or when you are studying a behavior that depends on a specific, rare feature with no redundant backup.