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Paper Citation Record · LEDGER

A Survey on Federated Causal Discovery and Inference

As of 9 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2606.23741.

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

pith.paper-citation-record.v1
2606.23741 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T10:23:40.906614Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

29 of 29 outbound references displayed

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  • verified fuzzy0
  • unresolved24
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation affc2457-0dc5-4c8d-a9c5-0873e4cbeea7 · outbound

This paper cites Causation, Prediction, and Search.

A Survey on Federated Causal Discovery and Inference Causation, Prediction, and Search

Reference 1

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Observation f19d5b6c-a5ad-4e01-9d82-ca05dfbf0ab0 · outbound

This paper cites Advances and open problems in federated learning.

A Survey on Federated Causal Discovery and Inference Advances and open problems in federated learning

Reference 2

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Observation c753c282-030e-4a96-bdab-4588a2022099 · outbound

This paper cites Learning Bayesian network structure from distributed homogeneous data.

A Survey on Federated Causal Discovery and Inference Learning Bayesian network structure from distributed homogeneous data

Reference 3

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Observation 60e483e5-2d54-47bb-8f18-eac3067da4e1 · outbound

This paper cites Federated causal discovery in medicine: Trends, opportunities, and challenges.

A Survey on Federated Causal Discovery and Inference Federated causal discovery in medicine: Trends, opportunities, and challenges

Reference 4

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Observation e70202a8-9111-42f8-944a-e9c1d1426869 · outbound

This paper cites Learning high-dimensional directed acyclic graphs with latent and selection variables.

A Survey on Federated Causal Discovery and Inference Learning high-dimensional directed acyclic graphs with latent and selection variables

Reference 5

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source=pdf_text observed=2026-06-26T10:23:40.906614Z digest=sha256:781384ea9996dbe2d32779fde41696c26f4ea158c1d245c6f87a0122c04034be

Observation 268d2d7a-6e93-4215-a302-f50f842ef5ae · outbound

This paper cites Gradient-based neural DAG learning.

A Survey on Federated Causal Discovery and Inference Gradient-based neural DAG learning

Reference 6

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Observation 907f9dd2-dc32-4eed-8d16-51829932ac68 · outbound

This paper cites Neural Granger causality.

A Survey on Federated Causal Discovery and Inference Neural Granger causality

Reference 7

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Observation 36e87ff6-d2d1-4079-862f-3c5f37871a9b · outbound

This paper cites A generalization of sampling without replacement from a finite universe.

A Survey on Federated Causal Discovery and Inference A generalization of sampling without replacement from a finite universe

Reference 8

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source=pdf_text observed=2026-06-26T10:23:40.906614Z digest=sha256:4a1c861ff3f3d53c4d47992d427b3374498d6bdab79ceca3cd7615d60998c7d6

Observation 7914e3ad-99a9-432f-9a12-48d50d24fa32 · outbound

This paper cites Tackling the objective inconsistency problem in heterogeneous federated optimization.

A Survey on Federated Causal Discovery and Inference Tackling the objective inconsistency problem in heterogeneous federated optimization

Reference 9

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Observation 1defda07-210a-4dc8-8664-e3751fa1f076 · outbound

This paper cites Communication-efficient federated learning via knowledge distillation.

A Survey on Federated Causal Discovery and Inference Communication-efficient federated learning via knowledge distillation

Reference 10

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Observation f71b31ad-1b6a-42ee-8450-5b5a64fa7e64 · outbound

This paper cites Crypten: Secure multi-party computation meets machine learning.

A Survey on Federated Causal Discovery and Inference Crypten: Secure multi-party computation meets machine learning

Reference 11

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Observation dbea7635-1144-46d3-8c2c-8f4acd81f548 · outbound

This paper cites A fully homomorphic encryption scheme.

A Survey on Federated Causal Discovery and Inference A fully homomorphic encryption scheme

Reference 12

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source=pdf_text observed=2026-06-26T10:23:40.906614Z digest=sha256:8a1c6fdd08ab1437c0efafbd911f937cededa5468478566e7a7f0c0d666d4945

Observation bdc76153-27a2-43f5-9ed7-8161f1caf724 · outbound

This paper cites Federated causal discovery from heterogeneous data.

A Survey on Federated Causal Discovery and Inference Federated causal discovery from heterogeneous data

Reference 13

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-26T10:23:40.906614Z digest=sha256:19fafb9aefff2a48db835e38027b945c5fcd8a91bfe72dddd571294d4cc3924e

Observation dd3d42f2-8684-46c2-a0f4-63374d40f45a · outbound

This paper cites Distributed Bayesian network structure learning.

A Survey on Federated Causal Discovery and Inference Distributed Bayesian network structure learning

Reference 14

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source=pdf_text observed=2026-06-26T10:23:40.906614Z digest=sha256:9bcfac2cbf641f92d15d1fec14307727c2f1de20383be451ff849ab762e46427

Observation c035e0c0-1d67-4cf2-b9fc-6f7c1cb4bf50 · outbound

This paper cites Federated learning of generalized linear causal networks.

A Survey on Federated Causal Discovery and Inference Federated learning of generalized linear causal networks

Reference 15

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Observation c88171aa-4830-4c42-9c6d-403cfee1d7f7 · outbound

This paper cites FedDAG: Federated DAG structure learning.

A Survey on Federated Causal Discovery and Inference FedDAG: Federated DAG structure learning

Reference 16

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source=pdf_text observed=2026-06-26T10:23:40.906614Z digest=sha256:fbe6240524cece26efc148b837ef527b0914df3ce62cb3193818a862772d5059

Observation b23ad3c5-ef37-4033-96e8-d73356672170 · outbound

This paper cites Interventional causal structure discovery over graphical models with convergence and optimality guarantees.

A Survey on Federated Causal Discovery and Inference Interventional causal structure discovery over graphical models with convergence and optimality guarantees

Reference 17

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Observation 62502456-3799-4457-9458-2d30d0180147 · outbound

This paper cites Federated local causal structure learning.

A Survey on Federated Causal Discovery and Inference Federated local causal structure learning

Reference 18

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source=pdf_text observed=2026-06-26T10:23:40.906614Z digest=sha256:0f50bb290ec0ac463e628de22f55eb2d580437c925ec887acf1b3845fb019c8b

Observation 5af72f1c-52f7-4c5e-81a8-b09486f54838 · outbound

This paper cites Federated causal structure learning with missing data.

A Survey on Federated Causal Discovery and Inference Federated causal structure learning with missing data

Reference 19

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source=pdf_text observed=2026-06-26T10:23:40.906614Z digest=sha256:7af68d6d7b887cb5d3a594a4b3f3e100fefd9aabd2c41e3b18623843bc9e3b41

Observation aa95605c-956c-41b4-94b8-4f288508b6e2 · outbound

This paper cites Federated multi-task Bayesian network learning in the presence of overlapping and distinct variables.

A Survey on Federated Causal Discovery and Inference Federated multi-task Bayesian network learning in the presence of overlapping and distinct variables

Reference 20

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source=pdf_text observed=2026-06-26T10:23:40.906614Z digest=sha256:baab9eac38f33d5d6abdc0f7be1c2d91779a701e5832ce198912876496a92312

Observation 802c035d-8917-4391-ae8f-45adf33ec3c2 · outbound

This paper cites Towards Uncertainty-Aware Federated Granger Causal Learning.

A Survey on Federated Causal Discovery and Inference Towards Uncertainty-Aware Federated Granger Causal Learning

Reference 21

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source=pdf_text observed=2026-06-26T10:23:40.906614Z digest=sha256:3380a9494fa449b81878b86bb688409a9415eac19073c725b82f992733f6aeff

Observation 447f12f3-fc7f-4d81-9418-6a82e3917bec · outbound

This paper cites An adaptive kernel approach to federated learning of heterogeneous causal effects.

A Survey on Federated Causal Discovery and Inference An adaptive kernel approach to federated learning of heterogeneous causal effects

Reference 22

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Observation 8588b434-0d07-4f9d-a586-ec5a3ec3f0cc · outbound

This paper cites Federated causal inference in heterogeneous observational data.

A Survey on Federated Causal Discovery and Inference Federated causal inference in heterogeneous observational data

Reference 23

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source=pdf_text observed=2026-06-26T10:23:40.906614Z digest=sha256:886cbc294a9bf2de5297fbab6ed1e28400f1612c7bb9405e0e8c0e1cbdac4e53

Observation da40b0f7-ced0-46ed-a982-fe3df89455e8 · outbound

This paper cites Federated Learning for Estimating Heterogeneous Treatment Effects.

A Survey on Federated Causal Discovery and Inference Federated Learning for Estimating Heterogeneous Treatment Effects

Reference 24

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source=pdf_text observed=2026-06-26T10:23:40.906614Z digest=sha256:5b33f0947c8bbba30fc286de313f5549974106393f568b1df670cad134b5487e

Observation 3a92df9a-ce99-49ed-94a9-51b190e03386 · outbound

This paper cites and Yang, S.

A Survey on Federated Causal Discovery and Inference and Yang, S

Reference 25

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arxiv_id, observed 2026-07-04T09:09:43.696236Z

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source=pdf_text observed=2026-06-26T10:23:40.906614Z digest=sha256:a899fbc52b472c0957ee50171b9696a5fb2c0fc01d3cdc295ab4294919639f6f

Observation 5998fd84-736a-48e2-be04-a993164fb8f6 · outbound

This paper cites Toward causal representation learning.

A Survey on Federated Causal Discovery and Inference Toward causal representation learning

Reference 26

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Observation 8b3592bd-0773-4c74-a6d2-a811d6e02bbf · outbound

This paper cites Dense: Data-free one-shot federated learning.

A Survey on Federated Causal Discovery and Inference Dense: Data-free one-shot federated learning

Reference 27

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source=pdf_text observed=2026-06-26T10:23:40.906614Z digest=sha256:3215e111c40e7ff94c7ad4c963d5994b00cf848f1dad5aef9a8c40b5d02452c3

Observation 33f9fd47-bd94-48dc-9774-d4b2b4377ce2 · outbound

This paper cites Integrating Large Language Model for Improved Causal Discovery.

A Survey on Federated Causal Discovery and Inference Integrating Large Language Model for Improved Causal Discovery

Reference 28

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arxiv_id, observed 2026-07-04T09:09:43.693052Z

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source=pdf_text observed=2026-06-26T10:23:40.906614Z digest=sha256:bea74045ef015dd4001c08bb413ba1f2ea2ba3b68fd323a8ebddbbffb094ed04

Observation 26b62b78-ad8e-4130-8dce-07e7a383f9fc · outbound

This paper cites Causality-based feature selection: Methods and evaluations.

A Survey on Federated Causal Discovery and Inference Causality-based feature selection: Methods and evaluations

Reference 29

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Pith citing papers

No inbound Pith citation observations are available.