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Paper Citation Record · LEDGER

Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.01103.

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

pith.paper-citation-record.v1
2306.01103 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:32:26.760278Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T01:03:59.396821Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c06c9477-41e7-4d36-b6f3-54422a2259a6 · inbound

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective cites this paper.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-09T11:32:26.760278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T11:32:26.760278Z digest=sha256:4923c2a0ef0a1f0b7a28bb2fe1ca769106dcd653fb800630541bf40f351e3992

Observation 62d00e3a-652f-43d0-831c-584f9d537328 · inbound

Learning Causality for Modern Machine Learning cites this paper.

Learning Causality for Modern Machine Learning Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

Reference 24

Resolution
verified exact
local_arxiv, observed 2026-08-07T01:03:59.466653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T01:03:48.567748Z digest=sha256:7f9df9d4825763a41982cdc342b3596d451603cef742b35d53cad366b38dd78a