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Paper Citation Record · LEDGER

Challenges of the inconsistency regime: Novel debiasing methods for missing data models

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2309.01362.

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

pith.paper-citation-record.v1
2309.01362 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:01:08.240136Z

measured 0 of 1 external citation measurements

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 c041b89d-112c-4ed1-8de9-c8ad7fecb9f1 · inbound

Uncertainty of high-dimensional genetic data prediction with polygenic risk scores cites this paper.

Uncertainty of high-dimensional genetic data prediction with polygenic risk scores Challenges of the inconsistency regime: Novel debiasing methods for missing data models

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:50.795759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:24:50.795759Z digest=sha256:dd4886060318dca073573214643b192ef2473cc5a0ad315d4459582a25fe2e93

Observation debd675f-bec5-4982-8d89-9985c2eb920c · inbound

State evolution beyond first-order methods I: Rigorous predictions and finite-sample guarantees cites this paper.

State evolution beyond first-order methods I: Rigorous predictions and finite-sample guarantees Challenges of the inconsistency regime: Novel debiasing methods for missing data models

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-15T18:01:08.240136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:01:08.240136Z digest=sha256:e746a873ded3cabfcbf858d3690a4e63113e56ad46366d40d5e72078fc3ec58d

Observation c6eff772-9610-481d-952a-e0f1ce5626d7 · inbound

Rescuing double robustness: safe estimation under complete misspecification cites this paper.

Rescuing double robustness: safe estimation under complete misspecification Challenges of the inconsistency regime: Novel debiasing methods for missing data models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-15T15:50:55.435438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T15:50:55.435438Z digest=sha256:9480f7a860f3db270524596f299c5015b4eaceeac96ad00c412851d23e17f599

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-08T15:35:08.202464Z digest=sha256:5d79fcdb24d6d726fe9b9269211d79490a680a38f36e62c41f87f4b63a05592f

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T23:25:55.375541Z digest=sha256:ca737363e51661b633813bc65f120ac23f170e5478c0a88d8f3ea80b4beb2b62

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 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-06-27T23:18:39.606271Z digest=sha256:c232923124681227f74493710dd96c8970e2761eecfdd66237ddcaf4ac0399c3

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.

source=arxiv_source observed=2026-08-01T07:16:11.953049Z digest=sha256:975bf0227909f8e212261cfb02cf178da063473abfe8abcb2cef90a5d30aad23