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Paper Citation Record · LEDGER

Can Large Language Models Help Experimental Design for Causal Discovery?

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.01139.

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

pith.paper-citation-record.v1
2503.01139 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:02:01.474857Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T17:33:45.265935Z

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 f10a2208-3832-4a67-96c9-3a62d22ec212 · 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 Can Large Language Models Help Experimental Design for Causal Discovery?

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-07T12:02:01.474857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:02:01.474857Z digest=sha256:b5c4426ec1387b983a8d59d61c61ef1e689655aa381f75ceb3f529738eaec613

Observation f938069c-3321-4f42-ae35-8b64e156e0c1 · 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 Can Large Language Models Help Experimental Design for Causal Discovery?

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-06-29T17:33:45.267757Z

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=sha256:18b0918964f47d2b10b2d345f9f69eae20a0271592df0c4433af90a19875d327