Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T17:42:33.634013Z
Paper Citation Record · LEDGER
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles
As of 18 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2507.10767.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
31 of 31 outbound references displayed
- verified exact0
- verified fuzzy28
- unresolved3
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 0231f200-43bb-4f1a-8341-7920e5ff1d32 · outbound
This paper cites Identification of partially observed linear causal models: Graphical conditions for the non- G aussian and heterogeneous cases.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Identification of partially observed linear causal models: Graphical conditions for the non- G aussian and heterogeneous cases
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.955131Z
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 29fd0364-7f1d-4a11-b3e4-466bb402356b · outbound
This paper cites Third-order moment varieties of linear Non- G aussian graphical models.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Third-order moment varieties of linear Non- G aussian graphical models
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.945204Z
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 aa6f4e56-0da5-4dfc-8e2b-2b607bbcea7a · outbound
This paper cites Controlling the false discovery rate: a practical and powerful approach to multiple testing.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Controlling the false discovery rate: a practical and powerful approach to multiple testing
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.935841Z
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 8f46c631-0b9e-4104-aa3d-8abebe105118 · outbound
This paper cites Causal discovery with latent confounders based on higher-order cumulants.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Causal discovery with latent confounders based on higher-order cumulants
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.926507Z
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 14c50201-c5de-45fd-b5ff-d0aad4c2bf1d · outbound
This paper cites Local causal discovery with linear non- G aussian cyclic models.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Local causal discovery with linear non- G aussian cyclic models
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.917282Z
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 fe7d4a3b-4e2c-4152-b053-f519341fcda0 · outbound
This paper cites Analyse générale des liaisons stochastiques: etude particulière de l'analyse factorielle linéaire.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Analyse générale des liaisons stochastiques: etude particulière de l'analyse factorielle linéaire
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.908355Z
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 7e5797ba-770a-4ce3-90b1-224555317968 · outbound
This paper cites The maximum likelihood threshold of a path diagram.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles The maximum likelihood threshold of a path diagram
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.899459Z
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 ed6cab78-319e-41c1-a599-e91bdd4041e8 · outbound
This paper cites Characterizing distribution equivalence and structure learning for cyclic and acyclic directed graphs.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Characterizing distribution equivalence and structure learning for cyclic and acyclic directed graphs
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.889647Z
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 403c157b-c5b2-4331-8517-042f01ed1625 · outbound
This paper cites Integrative modelling reveals mechanisms linking productivity and plant species richness.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Integrative modelling reveals mechanisms linking productivity and plant species richness
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.880438Z
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 532fdc4c-e316-473f-9641-d6ad5905ad29 · outbound
This paper cites A simple sequentially rejective multiple test procedure.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles A simple sequentially rejective multiple test procedure
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.871185Z
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 28f31e9a-b5fc-48a5-babb-e9ccad640ab6 · outbound
This paper cites GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:42:33.572602Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 276a60d0-29d8-4984-a34c-82b428470aca · outbound
This paper cites Ramsey, and Patrik O.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Ramsey, and Patrik O
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.862144Z
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 45b64ba5-72d5-4a7b-b3ca-9eba993140e8 · outbound
This paper cites Learning linear non- G aussian graphical models with multidirected edges.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Learning linear non- G aussian graphical models with multidirected edges
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.853459Z
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 18741a0a-b447-4f10-b1ce-1bad8abf30fd · outbound
This paper cites Handbook of graphical models.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Handbook of graphical models
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.844319Z
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 81797be9-74fb-4620-9d50-5a6c3c045bd0 · outbound
This paper cites Linear models: a useful ``microscope'' for causal analysis.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Linear models: a useful ``microscope'' for causal analysis
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.835694Z
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 3b1f6280-e325-4f74-9cb8-b57853a85b7d · outbound
This paper cites Py-tetrad and rpy-tetrad: A new python interface with r support for tetrad causal search.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Py-tetrad and rpy-tetrad: A new python interface with r support for tetrad causal search
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.827144Z
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 627f7233-3ef2-49fd-955c-6f4796c42319 · outbound
This paper cites A discovery algorithm for directed cyclic graphs.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles A discovery algorithm for directed cyclic graphs
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.817952Z
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 a7a30eb4-a634-4ef6-8968-1ecd4fc01fc6 · outbound
This paper cites A polynomial-time algorithm for deciding equivalence of directed cyclic graphical models.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles A polynomial-time algorithm for deciding equivalence of directed cyclic graphical models
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.809249Z
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 fc629534-3188-4533-85b7-6a7e326bba86 · outbound
This paper cites Multi-trek separation in linear structural equation models.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Multi-trek separation in linear structural equation models
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.800659Z
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 98ee90e9-53d0-4846-977c-58cc5d1b5285 · outbound
This paper cites Learning linear non- G aussian causal models in the presence of latent variables.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Learning linear non- G aussian causal models in the presence of latent variables
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.791302Z
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 172e900b-45fd-42eb-a4c5-296a0a49118d · outbound
This paper cites Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:42:33.604077Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation edf576f7-87fb-44b7-9720-6c38061896f3 · outbound
This paper cites Causal structure learning in directed, possibly cyclic, graphical models.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Causal structure learning in directed, possibly cyclic, graphical models
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.782109Z
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 885499a4-c937-4776-9dbc-390090eb0c3b · outbound
This paper cites Statistical causal discovery: L i NGAM approach.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Statistical causal discovery: L i NGAM approach
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.772601Z
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 c9be93e8-318e-4209-ac01-a99b4c64f3e6 · outbound
This paper cites arinen, and Antti.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles arinen, and Antti
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.763077Z
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 c8485339-4fa5-44aa-9b04-68e7b8174dfe · outbound
This paper cites Hoyer, and Kenneth Bollen.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Hoyer, and Kenneth Bollen
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.753844Z
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 84e2d6e4-d13e-4d23-8a5c-835d1f3d38b9 · outbound
This paper cites an unresolved cited work.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Unresolved cited work
Reference 26
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T17:42:33.743625Z
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 464e380a-b944-4d84-a7eb-1374f4c92807 · outbound
This paper cites Using path diagrams as a structural equation modeling tool.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Using path diagrams as a structural equation modeling tool
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.732079Z
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 435aeb6e-55fa-4bd3-bdee-56a7c73c56b2 · outbound
This paper cites Trek separation for G aussian graphical models.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Trek separation for G aussian graphical models
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.721881Z
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 69dd597d-5a43-49f2-95e6-763698cfeb8c · outbound
This paper cites Learning linear non- G aussian polytree models.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Learning linear non- G aussian polytree models
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.712015Z
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 fd435fcd-b4a8-4868-b4b1-0fed381b4b64 · outbound
This paper cites Samuel Wang and Mathias Drton.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Samuel Wang and Mathias Drton
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.701196Z
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 0adcaf13-e052-4258-9ec7-f0f8124b74d2 · outbound
This paper cites Samuel Wang and Mathias Drton.
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Samuel Wang and Mathias Drton
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:42:33.689035Z
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
No inbound Pith citation observations are available.