Paper Citation Record · LEDGER

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation

As of 18 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 1 inbound Pith citation observation for arXiv:2506.19082.

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

pith.paper-citation-record.v1
2506.19082 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:41:06.407326Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T12:11:28.700495Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T12:13:16.058336Z

Reference resolution

24 of 24 outbound references displayed

verified exact0
verified fuzzy12
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 03ef261d-d4f6-43a9-8a09-4e5e2c2971fb · outbound

This paper cites Synthetic Data -- what, why and how?.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Synthetic Data -- what, why and how?

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T18:41:06.301801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9b9162c-f748-426b-8666-9f6d850fbc42 · outbound

This paper cites A survey on bias and fairness in machine learning.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation A survey on bias and fairness in machine learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T18:41:06.307551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17353814-b787-41d2-9661-43dbae24a3ed · outbound

This paper cites The problem of fairness in synthetic healthcare data.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation The problem of fairness in synthetic healthcare data

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:41:06.750550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3b4f93bd-7585-449a-81b7-7b98abe01cc6 · outbound

This paper cites Modeling tabular data using conditional gan.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Modeling tabular data using conditional gan

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:41:06.735985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d3f3700a-342e-4617-b28c-ff998a6f11d7 · outbound

This paper cites Tabddpm: Modelling tabular data with diffusion models.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Tabddpm: Modelling tabular data with diffusion models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T18:41:06.320943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19adcd35-1303-40e1-a6b2-1f5b70fe5566 · outbound

This paper cites REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T18:41:06.325097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b79da38-81c1-42f7-905a-00ec07a62a17 · outbound

This paper cites Language Models are Realistic Tabular Data Generators.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Language Models are Realistic Tabular Data Generators

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T18:41:06.329864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c56bf0b-2c15-4ea0-be79-5bec76e154ce · outbound

This paper cites Tabfairgan: Fair tabular data generation with generative adversarial networks.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Tabfairgan: Fair tabular data generation with generative adversarial networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:41:06.711913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d61ce629-a890-4ff3-9ce6-85269fe2221f · outbound

This paper cites Counterfactual fairness.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Counterfactual fairness

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T18:41:06.338886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5380a8e0-f845-49fd-8ac4-8f640a672c30 · outbound

This paper cites Path-specific counterfactual fairness.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Path-specific counterfactual fairness

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:41:06.687275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e9142e40-454d-4c21-91c6-5803b6a0703d · outbound

This paper cites Fairgan: Fairness-aware generative adversarial networks.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Fairgan: Fairness-aware generative adversarial networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:41:06.673089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2f665dc4-328f-45ed-9c05-8b1398642e71 · outbound

This paper cites Achieving causal fairness through generative adversarial networks.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Achieving causal fairness through generative adversarial networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:41:06.658793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 83d14dd7-0ae2-4fee-b7ed-ec20e6720bf6 · outbound

This paper cites Fairness in decision-making—the causal explanation formula.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Fairness in decision-making—the causal explanation formula

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T18:41:06.356571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f99e279-41fa-43c3-8db4-449438133678 · outbound

This paper cites Learning optimal fair policies.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Learning optimal fair policies

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T18:41:06.360958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 120773f3-98d6-468b-83ef-89b962ecb26d · outbound

This paper cites Causal fairness analysis: a causal toolkit for fair machine learning.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Causal fairness analysis: a causal toolkit for fair machine learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:41:06.624005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b2d2641c-9337-4e79-8de4-11d4997a4380 · outbound

This paper cites Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T18:41:06.369652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3537d80-05b1-49b1-af74-3d21176f3ad8 · outbound

This paper cites Decaf: Generating fair synthetic data using causally-aware generative networks.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Decaf: Generating fair synthetic data using causally-aware generative networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:41:06.607434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d196b173-c8a2-40fa-acdd-e71dd0327ff4 · outbound

This paper cites Mitigating and assessing bias and fairness in large language model-generated synthetic tabular data.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Mitigating and assessing bias and fairness in large language model-generated synthetic tabular data

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:41:06.592054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f4b2f127-d4ca-4745-9dd2-4b4424bcb01e · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T18:41:06.384098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe1cd14b-07f4-4fad-8b5e-2f047dd76119 · outbound

This paper cites A survey on in-context learning.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation A survey on in-context learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T18:41:06.388915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa026032-0d84-4401-bb81-a4fe2d3a10a6 · outbound

This paper cites SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T18:41:06.393551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09d6e2f4-9253-4d4e-b1ed-2cbc00212ee7 · outbound

This paper cites fairadapt: Causal reasoning for fair data preprocessing.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation fairadapt: Causal reasoning for fair data preprocessing

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:41:06.567238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d8f0b0dc-2992-492e-bbb7-1fb4232b6088 · outbound

This paper cites Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:41:06.552486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4a61d8a4-b959-4348-93ba-ab5e89577e4d · outbound

This paper cites Advancing Ethical and Responsible AI: Exploring Fairness, Privacy, and Explainability through Causal Perspectives.

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Advancing Ethical and Responsible AI: Exploring Fairness, Privacy, and Explainability through Causal Perspectives

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:41:06.535741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 14ebadc0-2258-43f6-b1df-ea43a6f02eac · inbound

Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets cites this paper.

Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:13:16.059671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.