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Paper Citation Record · LEDGER

General targeted machine learning for modern causal mediation analysis

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2408.14620.

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

pith.paper-citation-record.v1
2408.14620 v2

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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-12T02:00:31.392696Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T08:20:58.362373Z

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 67222342-200a-4e69-b8ca-3419c82d96b9 · inbound

crumble: A comprehensive framework for modern causal mediation analysis with intermediate confounding cites this paper.

crumble: A comprehensive framework for modern causal mediation analysis with intermediate confounding General targeted machine learning for modern causal mediation analysis

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-11T08:20:58.370989Z

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.

source=pdf_text observed=2026-05-10T16:43:01.966102Z digest=sha256:61bab6a33398d4717087285e5704d362eb8bf2cb86e219ea40eae6a46402faba

Observation e89e06ea-1296-4069-9b07-66984d33c010 · inbound

Disentangling Causal Mechanisms in Conjoint Experiments Using Mediation cites this paper.

Disentangling Causal Mechanisms in Conjoint Experiments Using Mediation General targeted machine learning for modern causal mediation analysis

Reference 1

Resolution
unresolved
no resolver link, observed 2026-07-12T02:00:31.392696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T02:00:31.392696Z digest=sha256:5d7e6e5713d2ef3d850dedc88071017801b0e6e61cca0ff69880824168d229bf