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Paper Citation Record · LEDGER

Understanding Causality with Large Language Models: Feasibility and Opportunities

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2304.05524.

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

pith.paper-citation-record.v1
2304.05524 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:16:09.053272Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T17:33:45.291712Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 0b184a2c-2dc0-4177-aa13-6d12ab57d9a1 · inbound

CounterBench: Evaluating and Improving Counterfactual Reasoning in Large Language Models cites this paper.

CounterBench: Evaluating and Improving Counterfactual Reasoning in Large Language Models Understanding Causality with Large Language Models: Feasibility and Opportunities

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-23T03:12:28.519431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-23T03:09:38.961123Z digest=sha256:2bc6e6470023ae07fd89a2f3e51f82d6b0467b660ba4951a061782a362a32282

Observation 9746c3ea-e50b-4251-bcaf-661d4d6e2362 · inbound

Causal Distillation: Transferring Structured Explanations from Large to Compact Language Models cites this paper.

Causal Distillation: Transferring Structured Explanations from Large to Compact Language Models Understanding Causality with Large Language Models: Feasibility and Opportunities

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T14:16:09.053272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:16:09.053272Z digest=sha256:eb9938a82facce52b27347e06e0452ce091efc1381c184e29d5281faeffcf022

Observation 6e4676b5-52ac-4946-8494-474ce6a2e354 · 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 Understanding Causality with Large Language Models: Feasibility and Opportunities

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T12:01:56.146578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:01:56.146578Z digest=sha256:f0dab679ecc22089d47a93af413c65571ecfb5cfad683747090713f9d884d5a3

Observation ae79dc7d-0d44-44f5-b9eb-d951a02bfed7 · inbound

Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery cites this paper.

Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery Understanding Causality with Large Language Models: Feasibility and Opportunities

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-07T05:08:30.879310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:08:30.879310Z digest=sha256:9de443a0ba69c305adad205853cd27c499ccd1f2cc5b7c2c4268d743d5799113

Observation b0040b22-495e-4e1f-8a6e-dd244bb62f0d · inbound

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

Unveiling Causal Reasoning in Large Language Models: Reality or Mirage? Understanding Causality with Large Language Models: Feasibility and Opportunities

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-06T22:36:25.618569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:36:25.618569Z digest=sha256:3f59244e89854b8200ee09c4b06870a0b5081fdebb56d1e853353e4833c6de27

Observation 3b22c1ff-cd58-4293-8601-d1718142a747 · inbound

Linear-LLM-SCM: Benchmarking LLMs for Coefficient Elicitation in Linear-Gaussian Causal Models cites this paper.

Linear-LLM-SCM: Benchmarking LLMs for Coefficient Elicitation in Linear-Gaussian Causal Models Understanding Causality with Large Language Models: Feasibility and Opportunities

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-03T01:15:41.475092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T01:15:41.475092Z digest=sha256:c5d2fde55e623c62c2db8aa1cce9814d0d8ee475ab6b4d2bfec895d0363be344

Observation a18e4f41-be8f-4a88-b5e1-1290e67d5699 · inbound

BLINC: Context-Specific Causal Learning for Automated RAN Configuration cites this paper.

BLINC: Context-Specific Causal Learning for Automated RAN Configuration Understanding Causality with Large Language Models: Feasibility and Opportunities

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-12T10:01:29.095836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-07T08:16:47.401183Z digest=sha256:ec0820e718dfde821562411287822546575fcad4aae39b7dc3b07d02cb2acb66

Observation 36bdf60d-57e5-49ec-990a-f9f7f68170f7 · inbound

Why LLMs Fail at Causal Discovery and How Interventional Agents Escape cites this paper.

Why LLMs Fail at Causal Discovery and How Interventional Agents Escape Understanding Causality with Large Language Models: Feasibility and Opportunities

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-06-29T17:33:45.293859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-29T17:28:33.394661Z digest=sha256:e32d00eadf1e15e3f9334e78cf889ba31de0e40b1ba0a2f4b2ecf8c7f3eb37e9