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.
Detecting and quantifying causal associations in large nonlinear time series datasets
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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.