Paper Citation Record · LEDGER

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests

As of 4 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2508.14936.

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pith.paper-citation-record.v1
2508.14936 v3

Coverage vector

measured 43 of 43 reference resolution

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Outbound references

Observation fa2a4360-3894-4a36-8097-41be5b46526e · outbound

This paper cites HIPAA privacy rule and public health; guidance from CDC and the U.S.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests HIPAA privacy rule and public health; guidance from CDC and the U.S

Reference 1

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This paper cites Regulation (EU) 2016/679 of the European Parliament and of the Council.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Regulation (EU) 2016/679 of the European Parliament and of the Council

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Generative deep learning

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Unresolved cited work

Reference 4

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Unresolved cited work

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This paper cites Synthetic data in biomedicine via generative artificial intelligence.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Synthetic data in biomedicine via generative artificial intelligence

Reference 6

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This paper cites Synthetic data—what, why and how? Royal Society.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Synthetic data—what, why and how? Royal Society

Reference 7

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Unresolved cited work

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This paper cites Sys- tematic review of generative adversarial networks (GANs) for medical image classification and segmentation.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Sys- tematic review of generative adversarial networks (GANs) for medical image classification and segmentation

Reference 9

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This paper cites Diffusion models in medical imaging: a comprehensive survey.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Diffusion models in medical imaging: a comprehensive survey

Reference 10

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This paper cites Navigating tabular data synthesis research understanding user needs and tool capabilities.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Navigating tabular data synthesis research understanding user needs and tool capabilities

Reference 11

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This paper cites An evaluation of synthetic data generators implemented in the python library synthcity.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests An evaluation of synthetic data generators implemented in the python library synthcity

Reference 12

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This paper cites A note on the evaluation of generative models.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests A note on the evaluation of generative models

Reference 13

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This paper cites Tabular data generation: can we fool XGBoost? NeurIPS 2022 First Table Representation Workshop.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Tabular data generation: can we fool XGBoost? NeurIPS 2022 First Table Representation Workshop

Reference 14

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This paper cites Synthetic data generation for a longitudinal cohort study—evaluation, method extension and reproduction of published data analysis results.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Synthetic data generation for a longitudinal cohort study—evaluation, method extension and reproduction of published data analysis results

Reference 15

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This paper cites An evaluation of the replicability of analyses using synthetic health data.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests An evaluation of the replicability of analyses using synthetic health data

Reference 16

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This paper cites Adversarial random forests for density estimation and generative modeling.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Adversarial random forests for density estimation and generative modeling

Reference 17

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This paper cites Wright, David S.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Wright, David S

Reference 18

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Unresolved cited work

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This paper cites Synthcity: a benchmark framework for diverse use cases of tabular synthetic data.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Synthcity: a benchmark framework for diverse use cases of tabular synthetic data

Reference 20

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Unresolved cited work

Reference 21

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This paper cites Anthropometrische Messungen in der NAKO Gesundheitsstudie—mehr als nur Gr ¨oße und Gewicht [Anthropometric measures in the German Na- tional Cohort—more than weight and height].

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Anthropometrische Messungen in der NAKO Gesundheitsstudie—mehr als nur Gr ¨oße und Gewicht [Anthropometric measures in the German Na- tional Cohort—more than weight and height]

Reference 22

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This paper cites Lifestyle and metabolic risk factors in patients with early-onset myocardial infarction: a case-control study.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Lifestyle and metabolic risk factors in patients with early-onset myocardial infarction: a case-control study

Reference 23

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests ActiGraph cutpoints impact physical activity and sedentary behavior outcomes in young children

Reference 24

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Unresolved cited work

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This paper cites Birth order, caesarean section, or daycare attendance in relation to child-and adult-onset type 1 diabetes: results from the German National Cohort.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Birth order, caesarean section, or daycare attendance in relation to child-and adult-onset type 1 diabetes: results from the German National Cohort

Reference 26

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Framework and baseline examination of the German National Cohort (NAKO)

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Guelph Family Health Study: pilot study of a home- based obesity prevention intervention

Reference 28

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This paper cites Deep neural networks and tabular data: a survey.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Deep neural networks and tabular data: a survey

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This paper cites Generating synthetic data is complicated: know your data and know your generator.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Generating synthetic data is complicated: know your data and know your generator

Reference 30

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Ross Quinlan

Reference 31

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This paper cites Why do tree-based models still outperform deep learning on typical tabular data? Adv Neural Inf Process Syst, 35:507–520.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Why do tree-based models still outperform deep learning on typical tabular data? Adv Neural Inf Process Syst, 35:507–520

Reference 32

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets

Reference 33

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Random forests

Reference 34

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Mixture of distributions

Reference 35

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests An introduction to the bootstrap

Reference 36

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests The PHQ-9: validity of a brief depression severity measure

Reference 37

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests A brief measure for assessing generalized anxiety disorder: the GAD-7

Reference 38

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Fairness without imputation: a decision tree approach for fair prediction with missing values

Reference 39

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This paper cites Missing value imputation with adversarial random forests -- MissARF.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Missing value imputation with adversarial random forests -- MissARF

Reference 40

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Countarfactuals—generating plausible model-agnostic counterfactual explanations with adversarial ran- dom forests

Reference 41

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This paper cites ML-Doctor: holistic risk assessment of inference attacks against machine learning models.

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests ML-Doctor: holistic risk assessment of inference attacks against machine learning models

Reference 42

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Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests Unresolved cited work

Reference 43

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