Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Challenges of the inconsistency regime: Novel debiasing methods for missing data models
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2309.01362.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-01T07:16:11.953049Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T15:47:06.933640Z
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 029f0387-47a4-43fc-b931-bde6cd113349 · inbound
High-Dimensional Statistics: Reflections on Progress and Open Problems cites this paper.
High-Dimensional Statistics: Reflections on Progress and Open Problems Challenges of the inconsistency regime: Novel debiasing methods for missing data models
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T18:31:13.654707Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation ec7661cf-ac5e-4119-b432-1525ce94b144 · inbound
High-Dimensional Statistics: Reflections on Progress and Open Problems cites this paper.
High-Dimensional Statistics: Reflections on Progress and Open Problems Challenges of the inconsistency regime: Novel debiasing methods for missing data models
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T13:15:46.699093Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 5a4c8a96-f6df-4444-9252-5cb8f48847b0 · inbound
Optimally taming biases in black-box models for efficient semiparametric estimation cites this paper.
Optimally taming biases in black-box models for efficient semiparametric estimation Challenges of the inconsistency regime: Novel debiasing methods for missing data models
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T15:47:06.935146Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 2bb26930-e5d3-45f8-ac69-c4b035e6dfb6 · inbound
Optimal use of a black-box learner in semiparametric estimation cites this paper.
Optimal use of a black-box learner in semiparametric estimation Challenges of the inconsistency regime: Novel debiasing methods for missing data models
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T07:16:11.953049Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.