Prediction bottlenecks do not discover causal structure beyond what linear models, Lasso, and classical Granger/PCMCI methods achieve; intervention benefits are mostly sample-size confounds, leaving a standardized falsification benchmark as the main contribution.
Proceedings of the IEEE , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 2years
2026 2roles
background 1polarities
background 1representative citing papers
Predictive representation learning structurally favors encoding slower or less noisy environment modes over causal system modes, as shown by an impossibility theorem for linear-Gaussian dynamics and large-scale neural experiments.
citing papers explorer
-
Prediction Bottlenecks Don't Discover Causal Structure (But Here's What They Actually Do)
Prediction bottlenecks do not discover causal structure beyond what linear models, Lasso, and classical Granger/PCMCI methods achieve; intervention benefits are mostly sample-size confounds, leaving a standardized falsification benchmark as the main contribution.
-
The Predictive-Causal Gap: An Impossibility Theorem and Large-Scale Neural Evidence
Predictive representation learning structurally favors encoding slower or less noisy environment modes over causal system modes, as shown by an impossibility theorem for linear-Gaussian dynamics and large-scale neural experiments.