Paper Citation Record · LEDGER

Efficient Neural Causal Discovery without Acyclicity Constraints

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2107.10483.

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

pith.paper-citation-record.v1
2107.10483 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:53:25.576981Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 68cb2c7f-3c28-4da8-83de-1082591cb345 · inbound

When Additive Noise Meets Unobserved Mediators: Bivariate Denoising Diffusion for Causal Discovery cites this paper.

When Additive Noise Meets Unobserved Mediators: Bivariate Denoising Diffusion for Causal Discovery Efficient Neural Causal Discovery without Acyclicity Constraints

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T21:53:25.576981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f11f7b5c-42f7-4b20-a6b1-819f48103c29 · inbound

Goal-Oriented Sequential Bayesian Experimental Design for Causal Learning cites this paper.

Goal-Oriented Sequential Bayesian Experimental Design for Causal Learning Efficient Neural Causal Discovery without Acyclicity Constraints

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T18:49:57.406239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97b9e10d-5118-43a8-aa90-18e497b805c9 · inbound

CauScale: Neural Causal Discovery at Scale cites this paper.

CauScale: Neural Causal Discovery at Scale Efficient Neural Causal Discovery without Acyclicity Constraints

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T03:16:27.906937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaa954a2-9f24-472f-b7d4-0c12d23c45b1 · inbound

Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection cites this paper.

Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection Efficient Neural Causal Discovery without Acyclicity Constraints

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T10:01:52.582540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8b579a2e-625d-4b33-b9ba-534d432a4ad3 · inbound

Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection cites this paper.

Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection Efficient Neural Causal Discovery without Acyclicity Constraints

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:36:29.160339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c890d5f6-d734-4f82-ac44-d4c323d361ab · inbound

Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability cites this paper.

Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability Efficient Neural Causal Discovery without Acyclicity Constraints

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-07-04T05:59:37.847873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.