Paper Citation Record · LEDGER

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables

As of 16 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:1908.03932.

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

pith.paper-citation-record.v1
1908.03932 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:16:09.402000Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

24 of 24 outbound references displayed

verified exact0
verified fuzzy21
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 39848618-77d8-4a9e-a2d3-75de61269a66 · outbound

This paper cites Causality in linear nongaussian acyclic models in the presence of latent gaussian confounders.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Causality in linear nongaussian acyclic models in the presence of latent gaussian confounders

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:11.147559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d0e441e5-32f9-4f3e-b0ea-11daa37b6902 · outbound

This paper cites An introduction to the bootstrap.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables An introduction to the bootstrap

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:11.126358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5b4b1454-d96f-4ae1-8641-0a0e40483232 · outbound

This paper cites Discovering unconfounded causal relationships using linear non-gaussian models.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Discovering unconfounded causal relationships using linear non-gaussian models

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:11.109705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 743959f9-5ea1-45ca-ac5b-fc91b564b9f2 · outbound

This paper cites Identifiability, separability, and uniqueness of linear ica models.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Identifiability, separability, and uniqueness of linear ica models

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:11.091007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation caddaef3-11a5-4437-9934-803ade23653b · outbound

This paper cites Learning minimal latent directed information polytrees.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Learning minimal latent directed information polytrees

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:11.074134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0a8ef72b-995f-43b8-b3e7-64c43dbb502b · outbound

This paper cites Budgeted experiment design for causal structure learning.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Budgeted experiment design for causal structure learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:11.054213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d8e59933-c909-41d0-82fd-1fe0b6ec18a5 · outbound

This paper cites Estimation of causal effects using linear non-gaussian causal models with hidden variables.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Estimation of causal effects using linear non-gaussian causal models with hidden variables

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:11.032998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 153f0753-072e-4c1f-aafc-520a655cea3a · outbound

This paper cites Nonlinear causal discovery with additive noise models.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Nonlinear causal discovery with additive noise models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:11.013892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation caefb0e6-b603-40d5-afa3-49b24661fa0b · outbound

This paper cites Independent component analysis, volume 46.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Independent component analysis, volume 46

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:10.993303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e9a63fc4-7222-49f6-b6ef-d109fa61e8c9 · outbound

This paper cites Estimation of a structural vector autoregression model using non-gaussianity.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Estimation of a structural vector autoregression model using non-gaussianity

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:10.976093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e0225dbb-b73b-4715-b99d-fa10e980741e · outbound

This paper cites Information-geometric approach to inferring causal directions.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Information-geometric approach to inferring causal directions

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:10.956286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c11f659c-04ef-4872-b9f9-4680b979d0e8 · outbound

This paper cites Ica with reconstruction cost for efficient overcomplete feature learning.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Ica with reconstruction cost for efficient overcomplete feature learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:09.722443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1a6fbca9-7a3b-4727-8a47-432e09e33dbb · outbound

This paper cites Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:09.700948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 75653999-2598-4822-b096-8b20086a7d5d · outbound

This paper cites Causality.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Causality

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T14:16:09.328341Z

Source-reported events for the cited work

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Observation 85669ae6-4c1a-4214-828a-9f002d012c4b · outbound

This paper cites Identifiability of gaussian structural equation models with equal error variances.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Identifiability of gaussian structural equation models with equal error variances

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:09.660238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6161de09-7930-4938-bac0-c4e0039fea45 · outbound

This paper cites Causal inference by using invariant prediction: identification and confidence intervals.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Causal inference by using invariant prediction: identification and confidence intervals

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T14:16:09.349794Z

Source-reported events for the cited work

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Observation f3d6a759-0eea-422f-81ab-bb9694b2f5f5 · outbound

This paper cites Learning vector autoregressive models with latent processes.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Learning vector autoregressive models with latent processes

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:09.621103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4677c413-dda1-45e4-b5f8-372298ac0958 · outbound

This paper cites Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-gaussian distributions.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-gaussian distributions

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:09.601271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8738cf8b-6afa-4c02-a788-492f2ff49dba · outbound

This paper cites A linear non-gaussian acyclic model for causal discovery.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables A linear non-gaussian acyclic model for causal discovery

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:09.579772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7370a87c-4e3b-4b30-b5a0-1c83e69e399a · outbound

This paper cites Directlingam: A direct method for learning a linear non-gaussian structural equation model.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Directlingam: A direct method for learning a linear non-gaussian structural equation model

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:09.557883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 84a9b743-fc74-4040-a1d6-d112785fd609 · outbound

This paper cites Causation, prediction, and search.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Causation, prediction, and search

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-14T14:16:09.383786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4dbf7723-bf43-495e-a8be-8e9bd9c8266e · outbound

This paper cites Parcelingam: a causal ordering method robust against latent confounders.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Parcelingam: a causal ordering method robust against latent confounders

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:09.514799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9d37ad1e-f69a-4219-a367-6e3543f05081 · outbound

This paper cites On the identifiability of the post-nonlinear causal model.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables On the identifiability of the post-nonlinear causal model

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:09.494302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e25507b8-fc02-461e-8757-6d87ffb93d6a · outbound

This paper cites Causal discovery in the presence of distribution shift: Skeleton estimation and orientation determination.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Causal discovery in the presence of distribution shift: Skeleton estimation and orientation determination

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:09.463150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.