Paper Citation Record · LEDGER

Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Data

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2407.13054.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2407.13054 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:16:37.241828Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

3
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 747b401e-1afb-44fd-baec-ab18f78c9612 · inbound

Differentiable Causal Discovery For Latent Hierarchical Causal Models cites this paper.

Differentiable Causal Discovery For Latent Hierarchical Causal Models Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Data

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-12T10:16:37.241828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a4793fc-20d2-4965-b9df-4170eb7633b0 · inbound

Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data cites this paper.

Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Data

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-11T14:39:37.317662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abb523e4-1267-48cd-9d56-8f18ce5e3dcd · inbound

LLM Cannot Discover Causality, and Should Be Restricted to Non-Decisional Support in Causal Discovery cites this paper.

LLM Cannot Discover Causality, and Should Be Restricted to Non-Decisional Support in Causal Discovery Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Data

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T12:01:58.159121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0b6e112-fac9-4859-8959-7d6b2c1f3d75 · inbound

TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations cites this paper.

TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Data

Reference 164

Resolution: verified exact; arxiv_id, observed 2026-05-08T19:09:03.274491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0f8f0907-917f-4c53-a044-fc90e1c95901 · inbound

DKCD: Domain Knowledge-Enhanced Causal Discovery from Unstructured Data cites this paper.

DKCD: Domain Knowledge-Enhanced Causal Discovery from Unstructured Data Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Data

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-13T03:42:33.642219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 532df382-336d-4ca1-bebd-ca5ab090e728 · inbound

AutoCause: A Python framework that automates expert decisions in environmental time-series causal discovery cites this paper.

AutoCause: A Python framework that automates expert decisions in environmental time-series causal discovery Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Data

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T01:07:49.040967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.