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Paper Citation Record · LEDGER

Generating Synthetic Text Data to Evaluate Causal Inference Methods

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2102.05638.

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

pith.paper-citation-record.v1
2102.05638 v1

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 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:58:47.940457Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T19:52:06.514059Z

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 8dc49710-fb4e-4022-96ff-73a56496b5ef · inbound

Mitigating Sycophancy in Decoder-Only Transformer Architectures: Synthetic Data Intervention cites this paper.

Mitigating Sycophancy in Decoder-Only Transformer Architectures: Synthetic Data Intervention Generating Synthetic Text Data to Evaluate Causal Inference Methods

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-12T19:58:47.940457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T19:58:47.940457Z digest=sha256:28fc4df79a03622c742177b4d75245bcade2300ffdd7d65685281767fc013fc4

Observation 5af4cf4d-80d8-4bc6-a65f-c5e42cd332a7 · inbound

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

Political-LLM: Large Language Models in Political Science Generating Synthetic Text Data to Evaluate Causal Inference Methods

Reference 163

Resolution
verified exact
local_arxiv, observed 2026-08-11T19:52:06.519227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T19:52:04.494317Z digest=sha256:a71938f780a7132a7ee329a5e4e8b2eb4f2b3fcceaa24a558e857ea1502f09bd