Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-09T20:53:49.178153Z
Paper Citation Record · LEDGER
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment
As of 7 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2607.07065.
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 2026-08-07T06:34:17.273281+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
31 of 31 outbound references displayed
- verified exact7
- verified fuzzy24
- unresolved0
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation d5459d1c-9c74-4bc1-99d7-dec14492188f · outbound
This paper cites How effective are machine learning and doubly robust estimators in incorporating high-dimensional proxies to reduce residual confounding? Pharmacoepidemiology and Drug Safety.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment How effective are machine learning and doubly robust estimators in incorporating high-dimensional proxies to reduce residual confounding? Pharmacoepidemiology and Drug Safety
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.765909Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 35ac8440-c40d-4ec9-a0f4-b572d4c04ba2 · outbound
This paper cites Can we train machine learning methods to outperform the high- dimensional propensity score algorithm? Epidemiology.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Can we train machine learning methods to outperform the high- dimensional propensity score algorithm? Epidemiology
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.763805Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ee89245e-d0ee-437f-87a0-4e482bf9832f · outbound
This paper cites High-dimensional propensity score and its machine learning extensions in residual con- founding control.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment High-dimensional propensity score and its machine learning extensions in residual con- founding control
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.719123Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation aaa19cc4-8090-4e6f-9406-1f6fd9f7cb3a · outbound
This paper cites National Health and Nutrition Examination Survey Data, 2013–2018.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment National Health and Nutrition Examination Survey Data, 2013–2018
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.721125Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c29a22d6-7ade-4734-b924-61e357a362df · outbound
This paper cites Machine learning for improving high-dimensional proxy con- founder adjustment in healthcare database studies: An overview of the current literature.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Machine learning for improving high-dimensional proxy con- founder adjustment in healthcare database studies: An overview of the current literature
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.725206Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a7b9f353-4529-4200-93f8-e2a0f0e9c698 · outbound
This paper cites Collaborative-controlled LASSO for constructing propensity score-based estimators in high-dimensional data.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Collaborative-controlled LASSO for constructing propensity score-based estimators in high-dimensional data
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.730942Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation b32f26e7-f7fe-4711-a473-bf16764e4952 · outbound
This paper cites Targeted learning with an undersmoothed LASSO propen- sity score model for large-scale covariate adjustment in health-care database studies.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Targeted learning with an undersmoothed LASSO propen- sity score model for large-scale covariate adjustment in health-care database studies
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.732922Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 2defd4b3-6e43-48d6-b351-002b3ee43cfa · outbound
This paper cites Outcome-adaptive lasso: Variable selection for causal inference.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Outcome-adaptive lasso: Variable selection for causal inference
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.748324Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation eba8258c-b376-470d-9af2-970ab6807678 · outbound
This paper cites Covariate selection with group lasso and doubly robust estimation of causal effects.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Covariate selection with group lasso and doubly robust estimation of causal effects
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.759740Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 593f6b53-365e-43ba-bb26-b45fbd84056a · outbound
This paper cites The highly adaptive lasso estimator.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment The highly adaptive lasso estimator
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.767727Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f05a6a0a-3dfe-42ce-a2ac-0b7f10bd0c61 · outbound
This paper cites Highly adaptive LASSO: Machine learning that provides valid nonparametric inference in realistic models.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Highly adaptive LASSO: Machine learning that provides valid nonparametric inference in realistic models
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.752923Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e830c066-75f2-420a-85f8-feb7a01003d7 · outbound
This paper cites reader reaction to ‘outcome-adaptive lasso: Variable selection for causal inference’ by shortreed and ertefaie (2017).
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment reader reaction to ‘outcome-adaptive lasso: Variable selection for causal inference’ by shortreed and ertefaie (2017)
Reference 12
- Resolution
- verified exact
- doi, observed 2026-07-09T20:56:33.613234Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 2d84d159-4aa4-4595-b295-8f35acf2b639 · outbound
This paper cites Flexible collaborative estimation of the average causal effect of a treatment using the outcome-highly-adaptive lasso.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Flexible collaborative estimation of the average causal effect of a treatment using the outcome-highly-adaptive lasso
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.757461Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 65dacb3c-6e62-420c-a4f7-d1cb5ec3c3a7 · outbound
This paper cites Robust inference on the average treatment effect using the outcome highly adaptive lasso.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Robust inference on the average treatment effect using the outcome highly adaptive lasso
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.755271Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation dd49766b-a5c4-42e4-94bc-7e7026790594 · outbound
This paper cites Constructing inverse probability weights for marginal structural models.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Constructing inverse probability weights for marginal structural models
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.745935Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 97deb67a-9b4d-436a-adb6-a45bc9a699d7 · outbound
This paper cites Dealing with limited overlap in estimation of average treatment effects.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Dealing with limited overlap in estimation of average treatment effects
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.737497Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 16195d8d-202a-4c94-ab60-3c03e14b9dc4 · outbound
This paper cites Diagnosing and responding to violations in the positivity assumption.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Diagnosing and responding to violations in the positivity assumption
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.741273Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 404d1c96-5ece-468a-ab27-f1efc11bf926 · outbound
This paper cites Doubly robust estimation in missing data and causal inference models.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Doubly robust estimation in missing data and causal inference models
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.756401Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 2691be4b-1e55-4f97-b5c8-bc8ad253c831 · outbound
This paper cites Targeted maximum likelihood learning.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Targeted maximum likelihood learning
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.761686Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 450c32cc-0756-45bf-ada1-6f3ac9fee86c · outbound
This paper cites Targeted Learning: Causal Inference for Observational and Experimental Data.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Targeted Learning: Causal Inference for Observational and Experimental Data
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.736521Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 1b345478-b391-4453-a349-32174412c256 · outbound
This paper cites Data-Adaptive Selection of the Propensity Score Truncation Level for Inverse- Probability–Weighted and Targeted Maximum Likelihood Estimators of Marginal Point Treatment Effects.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Data-Adaptive Selection of the Propensity Score Truncation Level for Inverse- Probability–Weighted and Targeted Maximum Likelihood Estimators of Marginal Point Treatment Effects
Reference 21
- Resolution
- verified exact
- doi, observed 2026-07-09T20:56:33.623298Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation bf3fdab4-dbec-4ea8-ba5c-be28af36d30b · outbound
This paper cites Why does obesity cause diabetes? Cell metabolism.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Why does obesity cause diabetes? Cell metabolism
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.744531Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f55bdaf4-2dc5-4e35-9ce7-fe7644a0dc47 · outbound
This paper cites Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.750657Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 1098b1e3-a69a-44bf-b830-a2a36eb5ac0a · outbound
This paper cites Regularized regression versus the high- dimensional propensity score for confounding adjustment in secondary database analyses.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Regularized regression versus the high- dimensional propensity score for confounding adjustment in secondary database analyses
Reference 24
- Resolution
- verified exact
- doi, observed 2026-07-09T20:56:33.613452Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e54be667-15fa-4dd0-96e3-b146350f2c17 · outbound
This paper cites Using simulation studies to evaluate statistical methods.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Using simulation studies to evaluate statistical methods
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.723374Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e756decb-ad4a-41e6-9a30-82fe120e5a96 · outbound
This paper cites A cautionary note for plasmode simulation studies in the setting of causal inference.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment A cautionary note for plasmode simulation studies in the setting of causal inference
Reference 26
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T20:56:34.330795Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation fe242822-86a2-42d7-b2f4-152a36a08ccf · outbound
This paper cites Rsimsum: Summarise results from monte carlo simulation studies.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Rsimsum: Summarise results from monte carlo simulation studies
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.729158Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation fac13cce-36bb-40ad-b27b-9a451a03526f · outbound
This paper cites High-dimensional propen- sity score adjustment in studies of treatment effects using health care claims data.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment High-dimensional propen- sity score adjustment in studies of treatment effects using health care claims data
Reference 28
- Resolution
- verified exact
- doi, observed 2026-07-09T20:56:33.617396Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 88f06adf-9c3d-41ae-8eb1-3203cfa3757e · outbound
This paper cites Covariate selection in high-dimensional propen- sity score analyses of treatment effects in small samples.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Covariate selection in high-dimensional propen- sity score analyses of treatment effects in small samples
Reference 29
- Resolution
- verified exact
- doi, observed 2026-07-09T20:56:33.626306Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a84122f0-56dc-41b1-a838-02309f02bbfd · outbound
This paper cites Comparing the performance of propensity score methods in healthcare database studies with rare outcomes.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment Comparing the performance of propensity score methods in healthcare database studies with rare outcomes
Reference 30
- Resolution
- verified exact
- doi, observed 2026-07-09T20:56:33.620422Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 023e7d6b-2999-4109-87da-8cf29aee254d · outbound
This paper cites outcome-adaptive lasso: Variable selection for causal inference.
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment outcome-adaptive lasso: Variable selection for causal inference
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T20:56:34.717083Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Pith citing papers
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