Enhancing Convergent Cross Mapping: Simple Preprocessing for Noise-Resilient Causal Discovery
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".