Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Deep Neural Networks for Estimation and Inference
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1809.09953.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T14:53:43.975371Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-14T05:01:06.889932Z
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 cefcd007-0a69-4356-ad1f-276a82e6c9f1 · inbound
Estimation of Conditional Average Treatment Effects with High-Dimensional Data cites this paper.
Estimation of Conditional Average Treatment Effects with High-Dimensional Data Deep Neural Networks for Estimation and Inference
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T14:53:43.975371Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 59fb721e-e4b0-4fe6-9391-21abf150688a · inbound
Nonparametric estimation of causal heterogeneity under high-dimensional confounding cites this paper.
Nonparametric estimation of causal heterogeneity under high-dimensional confounding Deep Neural Networks for Estimation and Inference
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T11:37:30.850539Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e7e1c98a-c084-4cc7-99fc-35d0a778e92c · inbound
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations cites this paper.
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations Deep Neural Networks for Estimation and Inference
Reference 38
- Resolution
- verified exact
- local_arxiv, observed 2026-08-14T05:01:06.896013Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.