Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T06:45:07.760041Z
Paper Citation Record · LEDGER
Analytical and Bootstrap Confidence Intervals of Double Machine Learning: Simulation studies and an application to rural-urban difference in obesity prevalence
As of 17 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2607.29456.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
13 of 13 outbound references displayed
- verified exact4
- verified fuzzy0
- unresolved8
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation dc39f72b-c035-4e1f-87f3-0bdca2b239a7 · outbound
This paper cites Estimating Causal Effects with Double Machine Learning -- A Method Evaluation.
Analytical and Bootstrap Confidence Intervals of Double Machine Learning: Simulation studies and an application to rural-urban difference in obesity prevalence Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:45:07.211961Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 393af0e5-e614-4c7a-8125-e77fc75ff3dd · outbound
This paper cites Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions.
Analytical and Bootstrap Confidence Intervals of Double Machine Learning: Simulation studies and an application to rural-urban difference in obesity prevalence Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-08-03T06:49:09.329367Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 72b14635-68d9-49ff-b2bd-64814438a8a4 · outbound
This paper cites Statistical and Machine Learning Methods for Evaluating Trends in Air Quality under Changing Meteorological Conditions.
Analytical and Bootstrap Confidence Intervals of Double Machine Learning: Simulation studies and an application to rural-urban difference in obesity prevalence Statistical and Machine Learning Methods for Evaluating Trends in Air Quality under Changing Meteorological Conditions
Reference 9
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-03T06:45:07.516937Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b28c8377-a79b-4262-84e4-3db1f3508165 · outbound
This paper cites Root -N-Consistent Semiparametric Regression.
Analytical and Bootstrap Confidence Intervals of Double Machine Learning: Simulation studies and an application to rural-urban difference in obesity prevalence Root -N-Consistent Semiparametric Regression
Reference 1988
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:45:07.576484Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ae597cfb-c65c-4a30-8e61-e19ba68b1305 · outbound
This paper cites Regression Shrinkage and Selection Via the Lasso.
Analytical and Bootstrap Confidence Intervals of Double Machine Learning: Simulation studies and an application to rural-urban difference in obesity prevalence Regression Shrinkage and Selection Via the Lasso
Reference 1996
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:45:07.737556Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 108dd729-c6bc-4a9c-a33a-5f0948ea1f05 · outbound
This paper cites Random Forests.
Reference 2001
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:45:07.072141Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cb55c9d9-700b-41e9-b6e0-d90aebcfb66e · outbound
This paper cites Computing the Nearest Correlation Matrix --a Problem from Finance.
Analytical and Bootstrap Confidence Intervals of Double Machine Learning: Simulation studies and an application to rural-urban difference in obesity prevalence Computing the Nearest Correlation Matrix --a Problem from Finance
Reference 2002
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:45:07.409280Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 72418369-f4a0-4a19-bdf6-90e72c3eda20 · outbound
This paper cites Double/Debiased Machine Learning for Treatment and Structural Parameters.
Analytical and Bootstrap Confidence Intervals of Double Machine Learning: Simulation studies and an application to rural-urban difference in obesity prevalence Double/Debiased Machine Learning for Treatment and Structural Parameters
Reference 2018
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:45:07.148882Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cbe0d9dc-f3e8-4165-abe1-5fa7667bc873 · outbound
This paper cites Mlr3: A Modern Object-Oriented Machine Learning Framework in R.
Analytical and Bootstrap Confidence Intervals of Double Machine Learning: Simulation studies and an application to rural-urban difference in obesity prevalence Mlr3: A Modern Object-Oriented Machine Learning Framework in R
Reference 2019
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:45:07.483822Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4daf3d96-2e10-48ed-9d2d-203ff1bdbc79 · outbound
This paper cites Double Machine Learning with Gradient Boosting and Its Application to the Big N Audit Quality Effect.
Analytical and Bootstrap Confidence Intervals of Double Machine Learning: Simulation studies and an application to rural-urban difference in obesity prevalence Double Machine Learning with Gradient Boosting and Its Application to the Big N Audit Quality Effect
Reference 2020
- Resolution
- verified exact
- doi, observed 2026-08-03T06:49:09.025807Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation fe099a6c-416f-4972-bd5c-1c44866e0677 · outbound
This paper cites Bootstrap vs Asymptotic Variance Estimation When Using Propensity Score Weighting with Continuous and Binary Outcomes.
Analytical and Bootstrap Confidence Intervals of Double Machine Learning: Simulation studies and an application to rural-urban difference in obesity prevalence Bootstrap vs Asymptotic Variance Estimation When Using Propensity Score Weighting with Continuous and Binary Outcomes
Reference 2022
- Resolution
- verified exact
- doi, observed 2026-08-03T06:49:09.595495Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 38b91b0e-3bc6-446b-b70e-ea233139722a · outbound
This paper cites Improving the Finite Sample Estimation of Average Treatment Effects using Double/Debiased Machine Learning with Propensity Score Calibration.
Analytical and Bootstrap Confidence Intervals of Double Machine Learning: Simulation studies and an application to rural-urban difference in obesity prevalence Improving the Finite Sample Estimation of Average Treatment Effects using Double/Debiased Machine Learning with Propensity Score Calibration
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:45:07.001419Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 352c1650-eea0-4f36-af09-5f7a08538975 · outbound
This paper cites Double Robust Variance Estimation with Parametric Working Models.
Analytical and Bootstrap Confidence Intervals of Double Machine Learning: Simulation studies and an application to rural-urban difference in obesity prevalence Double Robust Variance Estimation with Parametric Working Models
Reference 2025
- Resolution
- verified exact
- doi, observed 2026-08-03T06:49:09.133768Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Pith citing papers
No inbound Pith citation observations are available.