Pith Number

pith:NJ2KMHGY

pith:2026:NJ2KMHGYZW55L3ZE76TEVCJKRK

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Beyond Coefficients: Forecast-Necessity Testing for Interpretable Causal Discovery in Nonlinear Time-Series Models

Dmitry Zaytsev, Michael Coppedge, Valentina Kuskova

Causal relevance in nonlinear time-series models is better judged by whether a link is required for accurate forecasts than by coefficient size.

arxiv:2604.18751 v1 · 2026-04-20 · cs.LG · cs.AI · stat.ME · stat.ML

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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Claims

C1strongest claim

causal relevance in nonlinear time-series models should be evaluated through forecast necessity rather than coefficient magnitude, and we present a practical evaluation procedure for doing so

C2weakest assumption

that systematic edge ablation combined with forecast comparison reliably identifies whether a candidate causal relationship is required for accurate prediction, without the ablation process itself introducing artifacts or biases

C3one line summary

Causal relevance in nonlinear time-series models is better assessed via forecast necessity through edge ablation and prediction comparison than via coefficient magnitudes, as illustrated on democracy panel data.

References

6 extracted · 6 resolved · 0 Pith anchors

[1] Albini, E., Long, J., Dervovic, D., & Magazzeni, D. (2022). Counterfactual shapley additive explanations. In Proceed- ings of the 2022 ACM conference on fairness, accountabil- ity, and transparency (p 2022

[2] Coppedge, M., Gerring, J., Knutsen, C.H., McMann, K., Mechkova, V ., Medzihorsky, J., Natsika, N., Neundorf, A., Paxton, P., Pemstein, D., von R ¨omer, J., Seim, B., Sigman, R., Skaaning, S.-E., Stato 2025

[3] Job, S., Tao, X., Cai, T., Xie, H., Li, L., Li, Q., & Yong, J. (2025). Exploring Causal Learning Through Graph Neu- ral Networks: An In-Depth Review. Wiley Interdisciplinary Reviews: Data Mining and K 2025

[4] Lim, N., d’Alch´e-Buc, F., Auliac, C., & Michailidis, G. (2015). Operator-valued kernel-based vector autoregressive models for network inference. Machine Learning, 99(3), 489–513. Mehdiyev, N., Enke, 2015

[5] D., & Cooch, E 2025

Receipt and verification

First computed	2026-05-27T01:04:58.114637Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6a74a61cd8cdbbd5ef24ffa64a892a8a952e67aea77d08fc51f9923fecfb8a64

Aliases

arxiv: 2604.18751 · arxiv_version: 2604.18751v1 · doi: 10.48550/arxiv.2604.18751 · pith_short_12: NJ2KMHGYZW55 · pith_short_16: NJ2KMHGYZW55L3ZE · pith_short_8: NJ2KMHGY

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/NJ2KMHGYZW55L3ZE76TEVCJKRK \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 6a74a61cd8cdbbd5ef24ffa64a892a8a952e67aea77d08fc51f9923fecfb8a64

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "180bdd44da24a97cf42c53775a783c08bec9025a6dca7b6e530953742af37ce2",
    "cross_cats_sorted": [
      "cs.AI",
      "stat.ME",
      "stat.ML"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-04-20T18:55:04Z",
    "title_canon_sha256": "dd9f079e63370cb5205f08f30d30c37bdf41157e669d60d085f325f74cf06a90"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.18751",
    "kind": "arxiv",
    "version": 1
  }
}