framework
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framework:counterfactual-latent-cl-lossCounterfactual Latent (CL) Loss
Auxiliary training objective from Grant (2025) that constrains intervened representations to remain near natural distribution
Neighborhood — ranked by edge-count
Papers (1)
paper
Thinkers (1)
thinker
- Satchel Grantstudies
Concepts (1)
concept
- Pre-recorded latent vector encoding the expected causal variable values post-intervention; used as ground truth in the CLMAS auxiliary loss.
Frameworks (1)
framework
- Modified CL LossextendsNovel variant of CL loss introduced in this paper targeting only causal subspace dimensions to improve OOD performance
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Auxiliary objective combining L2 and cosine losses against pre-recorded CL vectors to improve causal relevance when one model is causally inaccessible.
- The output value a model produces when an interchange intervention forces certain variables to take values from source inputs.
- MAS variant with an auxiliary CL loss objective for cases where one model is causally inaccessible, enabling ANN-BNN comparisons.
- The mental effort of holding models of how things could/should be different from actuality, contributing to compression stress.
- The behavior that would have occurred had the value of a causal variable been different while everything else remained the same; used as training labels in DAS/MAS.
- Encoding possible future or alternative states; bioelectric patterns can represent an alternative morphological target even in an intact animal.
- The state a neural network is placed in when its activations are modified via intervention