Paper Citation Record · LEDGER

Cause and Effect: Can Large Language Models Truly Understand Causality?

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2402.18139.

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

pith.paper-citation-record.v1
2402.18139 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:09:39.666961Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

2
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ccd6193a-75f3-4f86-a0f8-8aecd1956542 · inbound

Learning by Analogy: Enhancing Few-Shot Prompting for Math Word Problem Solving with Computational Graph-Based Retrieval cites this paper.

Learning by Analogy: Enhancing Few-Shot Prompting for Math Word Problem Solving with Computational Graph-Based Retrieval Cause and Effect: Can Large Language Models Truly Understand Causality?

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T13:09:39.666961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf5e4b15-a9ed-45b4-b30a-9f9d86c14527 · inbound

Political-LLM: Large Language Models in Political Science cites this paper.

Political-LLM: Large Language Models in Political Science Cause and Effect: Can Large Language Models Truly Understand Causality?

Reference 157

Resolution: unresolved; no resolver link, observed 2026-08-11T19:52:04.464497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2e31b06-b552-4d5b-a3f8-c10fbfb47c81 · inbound

Do LLMs Act as Repositories of Causal Knowledge? cites this paper.

Do LLMs Act as Repositories of Causal Knowledge? Cause and Effect: Can Large Language Models Truly Understand Causality?

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T15:48:59.890797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b953c06-4899-4316-8e09-4d9d98b7031f · inbound

Unveiling Causal Reasoning in Large Language Models: Reality or Mirage? cites this paper.

Unveiling Causal Reasoning in Large Language Models: Reality or Mirage? Cause and Effect: Can Large Language Models Truly Understand Causality?

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T22:36:20.250781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3462f19f-6220-49a1-a503-049fa24058ce · inbound

Think in Games: Learning to Reason in Games via Reinforcement Learning with Large Language Models cites this paper.

Think in Games: Learning to Reason in Games via Reinforcement Learning with Large Language Models Cause and Effect: Can Large Language Models Truly Understand Causality?

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-05T14:23:28.247417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.