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Paper Citation Record · LEDGER

Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities

As of 20 August 2026, this Paper Citation Record lists 2 of 2 outbound references and 1 inbound Pith citation observation for arXiv:2604.23904.

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

pith.paper-citation-record.v1
2604.23904 v3

Coverage vector

measured 2 of 2 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T15:34:38.524816Z

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-25T19:03:59.234023Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-04T21:10:09.225442Z

Reference resolution

2 of 2 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 85a93ad6-e03e-4d1f-aa30-613db10d47b3 · outbound

This paper cites Large Language Models and Causal Inference in Collaboration: A Survey.

Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities Large Language Models and Causal Inference in Collaboration: A Survey

Reference 2007

Resolution
unresolved
no resolver link, observed 2026-08-02T15:34:38.524816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T15:34:38.524816Z digest=sha256:f3536a88f5f3c0c5c10e478e1580baf0e7962fdff9a15ff2be04006e6df9254c

Observation 4eafd3ed-4529-47ad-91e6-6c7744a52aa6 · outbound

This paper cites doi: 10.1093/aje/kwac087.

Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities doi: 10.1093/aje/kwac087

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-02T15:34:38.519625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T15:34:38.519625Z digest=sha256:9d615a3ffe6a6531194b196d8f7f695fea35869d365b608e7c9aa01eadb8957b

Pith citing papers

Observation bab0330e-f3e3-4ec5-9d26-4fdd5d479b22 · inbound

When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification? cites this paper.

When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification? Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities

Reference 135

Resolution
metadata mismatch
local_arxiv, observed 2026-07-04T21:10:09.226891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-06-25T19:03:59.234023Z digest=sha256:5c604789fa46cbec85979c07147ef5b542238c5dbb8e6aac81dab1109ec5488f