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Paper Citation Record · LEDGER

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration

As of 7 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2607.08122.

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

pith.paper-citation-record.v1
2607.08122 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T08:00:21.964524Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

19 of 19 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7df78db9-bfaf-4b51-85c9-bf77cc9e0442 · outbound

This paper cites Liang, and Chao Yan.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration Liang, and Chao Yan

Reference 1

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source=pdf_text observed=2026-08-02T08:00:19.695648Z digest=sha256:11e7486e08bc640f01756d18ad7509fcae6fb629bf68bfe46fc0f4b870c56b4b

Observation 6e6341a5-ffda-44cb-b204-62e4667a8a2b · outbound

This paper cites an unresolved cited work.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration Unresolved cited work

Reference 2

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source=pdf_text observed=2026-08-02T08:00:21.512638Z digest=sha256:c12aa5c921e87721a33206c26c1087640a2e43e5b0850f5001c17a8280b3397f

Observation 18f045eb-8e58-4d69-b6af-25b0a5b85acc · outbound

This paper cites an unresolved cited work.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration Unresolved cited work

Reference 3

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source=pdf_text observed=2026-08-02T08:00:21.353199Z digest=sha256:efc89dd8c147fc813313181fb4f1315d36f80f7890d9a1da9f79c4e4ecf30845

Observation 76baef2c-961f-4e7f-a96a-952b3fb1cd5d · outbound

This paper cites Christos Louizos, Uri Shalit, Joris M.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration Christos Louizos, Uri Shalit, Joris M

Reference 6

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source=pdf_text observed=2026-08-02T08:00:20.126580Z digest=sha256:ab67bb260a5030a90380f3040957cd1def2b6ead650ad6514c69814bcbbfd9dc

Observation 56dd2e3a-20f4-4fb6-8f76-3d97b88f50c8 · outbound

This paper cites Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, and Aadharsh Kannan.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, and Aadharsh Kannan

Reference 9

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source=pdf_text observed=2026-08-02T08:00:20.474505Z digest=sha256:c33da6c2793973f5f1195359092da43330879a57ec770afe199e27d178e5ecec

Observation c6cdf627-b2c8-4451-8324-24d4936af956 · outbound

This paper cites Jeffrey W.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration Jeffrey W

Reference 10

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source=pdf_text observed=2026-08-02T08:00:20.355355Z digest=sha256:a48a94e8fdc9e800671c939d5463263eb273695de9e9be99933026526b95c86e

Observation 917c46d2-ea24-47a6-9a83-30530789ebd7 · outbound

This paper cites an unresolved cited work.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration Unresolved cited work

Reference 14

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source=pdf_text observed=2026-08-02T08:00:21.185120Z digest=sha256:f57566af94f45cb68c638a48b60688050652fbeae72eb192cb7da028cd4d7662

Observation 7055e8f6-b9b7-4144-9919-e7169c222198 · outbound

This paper cites The bias-aware variant of Appendix F replaces TM by TM + \Approx(ϕ)2.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration The bias-aware variant of Appendix F replaces TM by TM + \Approx(ϕ)2

Reference 17

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source=pdf_text observed=2026-08-02T08:00:21.665887Z digest=sha256:6f1aaca9ba2e2f85b9796c8dc64d7b21f455bf825294c5098e6b66773ac2ef5b

Observation 26164815-3a4b-4b4c-bb33-77bd80d6ba37 · outbound

This paper cites •Synthetic sample size: varyn syn/n∈ {1,2,5,10,50}.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration •Synthetic sample size: varyn syn/n∈ {1,2,5,10,50}

Reference 18

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Observation 1c8e784c-7829-4f30-bf98-e071722d6206 · outbound

This paper cites Rows index sample size n∈ {1000,5000,20000} and columns index ε∈ {0.5,1,2,5}.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration Rows index sample size n∈ {1000,5000,20000} and columns index ε∈ {0.5,1,2,5}

Reference 19

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Observation 5237cb67-3a01-4ded-a294-1f595c35b4c4 · outbound

This paper cites Model agnostic differentially private causal inference.arXiv preprint arXiv:2505.19589,.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration Model agnostic differentially private causal inference.arXiv preprint arXiv:2505.19589,

Reference 1986

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Observation 00a30c39-e216-4de6-9d89-aabff20015f2 · outbound

This paper cites PrivATE: Differentially private confidence in- tervals for average treatment effects.arXiv preprint arXiv:2505.21641, 2025a.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration PrivATE: Differentially private confidence in- tervals for average treatment effects.arXiv preprint arXiv:2505.21641, 2025a

Reference 1987

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Observation 8778459c-1acc-402a-92b1-228c9408d177 · outbound

This paper cites Benchmarking Differentially Private Synthetic Data Generation Algorithms.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration Benchmarking Differentially Private Synthetic Data Generation Algorithms

Reference 2011

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Observation b0877fae-8734-4918-a4be-4be77ee3dee9 · outbound

This paper cites Claire McKay Bowen and Fang Liu.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration Claire McKay Bowen and Fang Liu

Reference 2016

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Observation 5a3314c2-9991-47bf-a119-ecda11af0358 · outbound

This paper cites Moritz Hardt, Katrina Ligett, and Frank McSherry.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration Moritz Hardt, Katrina Ligett, and Frank McSherry

Reference 2017

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source=pdf_text observed=2026-08-02T08:00:19.884609Z digest=sha256:45b18d37691338e7bae80546561446074ca03b7c7cc213525f51b44c320103c2

Observation 0b57a3c2-db98-45f9-9317-ff825b0db5b1 · outbound

This paper cites Winning the NIST Contest: A scalable and general approach to differentially private synthetic data.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration Winning the NIST Contest: A scalable and general approach to differentially private synthetic data

Reference 2019

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source=pdf_text observed=2026-08-02T08:00:20.235477Z digest=sha256:2c0e218148814b42ea0bf99b1a830a0e4eae7e6171a088b5f3462dce226e0c2c

Observation f5e92107-d8e3-486b-9d0e-9344c59eb341 · outbound

This paper cites Differentially Private Covariate Balancing Causal Inference.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration Differentially Private Covariate Balancing Causal Inference

Reference 2022

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Observation e18c71b6-5ad6-4934-ad5f-0285d6144480 · outbound

This paper cites Trivellore E.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration Trivellore E

Reference 2024

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source=pdf_text observed=2026-08-02T08:00:20.798782Z digest=sha256:628f41487b0a9b9f79be12ac79923797b8f605195e4dd1a1f9da80467badb651

Observation 270943e4-a011-4736-bec0-8bc55e6d3e4b · outbound

This paper cites Preprint; PMCID: PMC13015641.

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration Preprint; PMCID: PMC13015641

Reference 2026

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Pith citing papers

No inbound Pith citation observations are available.