Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Conditional cross-fitting for unbiased machine-learning-assisted covariate adjustment in randomized experiments
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2508.15664.
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 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-01T12:16:02.091122Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T21:26:14.255156Z
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 83a6193e-52bd-4f78-a2ef-862cb7b2d8ce · inbound
Design-based edge-level causal inference with machine learning assisted covariate adjustment cites this paper.
Design-based edge-level causal inference with machine learning assisted covariate adjustment Conditional cross-fitting for unbiased machine-learning-assisted covariate adjustment in randomized experiments
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T21:26:14.256604Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation d43b874e-1214-403d-be8a-33798c883175 · inbound
GAUGER: Generalized Regression Adjustment via Graph-Weighted Exposure-Level Residualization for Design-Based Inference Under Interference cites this paper.
GAUGER: Generalized Regression Adjustment via Graph-Weighted Exposure-Level Residualization for Design-Based Inference Under Interference Conditional cross-fitting for unbiased machine-learning-assisted covariate adjustment in randomized experiments
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T12:16:02.091122Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.