Enhancing Convergent Cross Mapping: Simple Preprocessing for Noise-Resilient Causal Discovery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Detecting causality in coupled nonlinear dynamical systems is challenging for the classic Granger Causality (GC) paradigm due to mirage correlations arising from coupling effects. Convergent Cross Mapping (CCM) was introduced as a model-free alternative to complement GC in such scenarios, yet its performance can deteriorate considerably in the presence of noise. Many studies on cross-mapping-based causal discovery assess their models using only noise-free or minimally noised simulated systems, overlooking real-world data that are often susceptible to significant noise. To address this gap, we examine the noise sensitivity of CCM and demonstrate how simple preprocessing with averaging filter can enhance its robustness. Through experiments on the noisy Lorenz system and the realworld weather dataset ERA5, we provide insights into filter parameter selection and its impact on inference quality, offering practical guidance for noisy causal inference in nonlinear systems. Additionally, we hypothesize that in the context of the systems we study, causal information may reside predominantly in lower-frequency domains, explaining why averaging filters-by removing high-frequency noise-improve causal inference.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it