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Paper Citation Record · LEDGER

De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1802.08667.

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

pith.paper-citation-record.v1
1802.08667 v6

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 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T06:04:35.555043Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T02:45:58.163768Z

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 d9dfa207-4a2e-41eb-80b1-cb9f9ce3cdd1 · inbound

Learning High-dimensional Gaussians from Censored Data cites this paper.

Learning High-dimensional Gaussians from Censored Data De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-16T06:04:35.555043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T06:04:35.555043Z digest=sha256:72a7450c00809c6d6cc4dcf129b006498c51c6aa44c06ed65d37503bd565227b

Observation 8bdba3c7-6ccf-44ba-9dee-87b52c547258 · inbound

BAMIFun: Bayesian Multiple Imputation for Functional Data cites this paper.

BAMIFun: Bayesian Multiple Imputation for Functional Data De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers

Reference 68

Resolution
verified exact
arxiv_id, observed 2026-05-11T02:45:58.167935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-11T02:42:43.813877Z digest=sha256:5d6cb0a8f8cd37b65c922af44814c6c5f6e17f7a7c852d12815210ff80fbe8dc