FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation

Amir M. Rahmani; Nitish Nagesh; Ziyu Wang

REVIEW 4 major objections 5 minor 24 references

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that an LLM-augmented pipeline can generate synthetic health data whose causal fairness metrics stay within 10% of the real data and that training a causally fair predictor on that data reduces direct-effect bias by more…

desk verdict The paper's new combination—LLM-based synthetic health data with causal fairness—is real, but its headline '<10% deviation' claim is contradicted by its own Table 1. read the letter →

arxiv 2506.19082 v1 pith:T5RFBKWY submitted 2025-06-23 cs.LG cs.AI

Nitish Nagesh , Ziyu Wang , Amir M. Rahmani This is my paper · ORCID

classification cs.LGcs.AI

keywords causalfairnesssyntheticdatagenerationlargelanguagemodelstabularhealthstandardmodeldirecteffectheartfailureclinicalrecordsFairCauseSyn

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to build the first synthetic-data pipeline that uses a large language model to generate health records with causal fairness built in, not just counterfactual fairness. It claims that on heart-failure records, the synthetic data tracks the real data on the causal fairness metrics—total variation, direct effect, indirect effect, and spurious effect—with deviations below 10% for the first three. It further claims that a causally fair predictor trained on the synthetic data cuts the direct effect of sex on survival by more than 70% relative to training on real data. The reason this matters is that fair synthetic health data could let researchers develop and audit prediction models without exposing real patient records.

What carries the argument

The load-bearing object is the Standard Fairness Model (SFM), a four-variable causal graph with protected attribute $X$, demographic confounder $Z$, mediators $W$, and outcome $Y$, together with the path-specific decomposition of total variation into direct, indirect, and spurious effects. Around it the pipeline wraps an LLM-based generate-and-check loop: curated real rows are turned into prompts, the LLM produces candidate synthetic rows, and a constraint-satisfaction module rejects batches until fairness and fidelity constraints pass. The FairAdapt causally fair predictor is the downstream device used to show that models trained on the synthetic data inherit lower direct and indirect discrimination.

What would settle it

Recompute the absolute differences between the Real and Synthetic rows in Table 1: the Indirect Effect row alone (real −0.0472, synthetic −0.0002) differs by about 0.047, which is not below 10% of the real value by the usual relative measure, so the 'below 10%' conclusion stands or falls on exactly which comparison the authors intend.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that causal fairness can be embedded into LLM-based tabular synthesis for health data: the FairCauseSyn pipeline assigns a Standard Fairness Model graph with protected attribute $X$, demographic confounder $Z$, mediators $W$, and outcome $Y$, decomposes observed discrimination into direct, indirect, spurious, and total effects, and then drives an LLM through prompt tuning and iterative constraint checks until the synthetic batch satisfies fidelity and fairness constraints. Evaluated on 299 heart-failure patients, the pipeline yields synthetic data whose causal-fairness profile mirrors the real data, and when a causal-fairness-constrained predictor is trained on the synthetic data, direct and indirect effects approach zero and the direct effect falls by over 70% compared with the real-data baseline. The authors present this as the first demonstration of causal-fairness-preserving synthetic health data generation.

Load-bearing premise

The fairness measurements assume the paper's causal diagram of the heart-failure data—sex affecting survival directly and through clinical features, with age as a confounder, and no hidden common causes—is the true one, and that these effects can be identified from the observed records.

Editorial extensions

If this is right

If the 10% claim holds, synthetic versions of sensitive health datasets can be released for fairness auditing in place of the real records.
If the 70% direct-effect reduction holds, synthetic data plus a causally fair training procedure could reduce sex-based disparities in clinical prediction models.
The pipeline gives LLM-based generation a causal-fairness objective rather than only statistical fidelity, so synthetic-data quality is measured by path-specific effects, not just distributional similarity.
The approach positions causal fairness, not counterfactual fairness, as the appropriate standard for health-data synthesis, since it separates direct, mediated, and confounded pathways.
The reported low standard deviations on direct and indirect effects suggest stable estimates, while the high spurious-effect variability indicates that controlling spurious influences remains an open problem the authors flag for future work.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The fairness numbers inherit the hand-assigned SFM graph; a natural extension is to re-run the pipeline under alternative graphs or with causal discovery to see whether the 10% and 70% results are graph-sensitive.
Because the dataset has only 299 patients and one protected attribute, the pipeline's practical value will depend on tests with larger, multi-site clinical datasets and additional sensitive attributes such as race or ethnicity.
The constraint loop could be adapted to target spurious-effect stability explicitly, since high spurious-effect variance is the residual weakness the paper acknowledges.
If the approach transfers, the same generate-check loop could be used for non-health tabular settings where causal fairness matters, such as credit or hiring, but the paper does not test those domains.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

First thing to know: this is a clearly written, well-scoped paper that addresses a real gap—nobody has combined LLM-based tabular synthesis with causal fairness metrics on health data. That novelty is real, but modest. The execution, however, undercuts the paper's own headline: the claim that generated data deviates by under 10% on TV, DE, and IE is directly contradicted by Table 1. Using any reasonable definition of deviation, TV changes from −0.012 to −0.049 and IE from −0.047 to −0.0002; those are roughly 300% and 100% relative changes. The paper never defines 'deviation', so the claim is unverifiable at best and false at worst.

What the paper does well: the SFM framework is correctly described, the choice of heart failure data is sensible, and the authors are honest about the high variability in spurious effects. The pipeline description, while high-level, shows awareness of the constraint-satisfaction loop.

The soft spots are mostly around evaluation. First, the constraint satisfaction module filters generated batches for fairness, then the paper evaluates success on those same fairness metrics—that's circular. No held-out protocol is reported. Second, the causal graph (sex→clinical features→survival, with age as confounder) is asserted without structural equations or identifiability discussion; all DE/IE/SE numbers inherit that assumption. Third, the '70% reduction in direct effect' is arithmetically true for one point estimate, but with overlapping standard deviations and no significance test it's not demonstrated. Fourth, no utility metrics are reported; a fair synthetic dataset that doesn't support downstream prediction is not useful.

The paper is the kind of work I'd want to see rigorously revised rather than quietly buried: the combination is new and the problem is important. But as submitted, the central empirical claim doesn't hold, and no code or prompts are provided to check the numbers. A serious referee should ask for a corrected deviation definition, a reproducibility release, utility benchmarks, and a justification of the causal graph. I'd take a skeptical look at acceptance, but I would engage with a revision.

Referee Report

4 major / 5 minor

Summary. The paper proposes FairCauseSyn, an LLM-augmented pipeline for generating synthetic tabular health data subject to causal fairness constraints. It uses a structural causal model (sex X, age Z, clinical features W, survival Y) and the path-specific fairness metrics TV, DE, IE, and SE. The pipeline prompts an LLM with data subsets, iteratively refines batches through a constraint satisfaction module, and evaluates the final data and predictions of a baseline random forest and a FairAdapt causally fair model on a 299-patient heart failure dataset. The headline claims are that the synthetic data deviates less than 10% from real data on TV, DE, and IE, and that training on synthetic data reduces DE by over 70%.

Significance. If the numerical claims were supported, this would be a useful first demonstration of LLM-based synthetic generation with causal fairness for health data. The paper also clearly identifies a gap in the literature and connects to standard causal fairness machinery. However, the central evaluation claim is not supported by the reported numbers, and the evaluation protocol is circular with respect to the fairness metrics. As written, the contribution is an unverified pipeline proposal rather than a validated method, and no code or data release is mentioned that would allow independent verification.

major comments (4)

[Table 1, Section 3.4.1, Section 4] The central claim that deviations in Total Variation (TV), Direct Effect (DE), and Indirect Effect (IE) are consistently below 10% is contradicted by Table 1. For the Data Fairness row, computing |synthetic - real| / |real| gives approximately 307% for TV, 99.6% for IE, and 10.1% for DE; the sign of IE flips from -0.0472 to -0.0002. No definition of 'deviation' is given in the paper, and no computation from the reported means and standard deviations yields a value below 10% for the stated metric set. This invalidates the abstract, the concluding paragraph in Section 4, and the interpretation of the data fairness results in Section 3.4.1.
[Section 2.4, Section 3.3, Section 3.4] The constraint satisfaction module in Section 2.4 iteratively refines or filters LLM-generated batches until fairness and fidelity constraints are met, and the evaluation in Sections 3.3 and 3.4 then reports closeness on those same causal fairness metrics. This makes the reported 'deviation below 10%' a fitted property of the optimization loop rather than an independent assessment. A held-out protocol is needed: constraints should be enforced on a development set and the final data evaluated on a separate set, or the metric used for selection should be reported separately from the metric used for evaluation. Without this, the deviation numbers cannot support the paper's external-validity claims.
[Section 3.2, Figure 1] The standard fairness model (SFM) graph is asserted without the structural equations or the identifiability assumptions needed for path-specific effects. The paper assigns X = sex, Z = age, W = all clinical features, Y = survival, and assumes edges X→Y, X→W→Y, Z→W→Y, Z→Y, and X↔Z, but it never specifies functional forms, the direction of the X↔Z association, or the absence of hidden confounders. All DE, IE, and SE estimates in Table 1 inherit this graph, so if the graph is misspecified the metrics do not measure causal fairness. The authors should provide the full SCM specification, state identifiability conditions, and ideally include a sensitivity analysis over plausible graphs.
[Section 3.4.3, Table 1] The claim of a 'reduction in direct effect exceeding 70%' compares two single point estimates (-0.0070±0.0016 vs -0.0020±0.0030) with no significance test or confidence-interval analysis across the real and synthetic conditions. In the same row, IE changes from -0.0538 to +0.0076, so the text's claim of 'near-zero direct and indirect effects' is not supported for IE. The conclusion should either report paired statistical comparisons with uncertainty quantification or soften the claim to a descriptive observation.

minor comments (5)

[Section 2.2] The text contains several typos, including 'afffecting' in the description of mediators and 'spurius' in the Future Work paragraph of Section 4.
[Equations (1)-(3)] The notation in Equations (1)-(3) is hard to parse, particularly the conditioning sets such as P(y_x0|x1) and the mixture of x0 and x1 subscripts; please define all notation explicitly and check the indices for consistency with the surrounding text.
[Section 3.2] The variable 'follow-up period' is listed as a mediator W, but the dataset description treats follow-up duration as an outcome-related variable; the role of this variable in the causal graph should be clarified.
[Table 1] Table 1 reports means ± standard deviations but no sample sizes, number of repetitions, or statistical tests, so the reader cannot assess the precision of the estimates or whether the real-synthetic differences are meaningful.
[References] The reference for the LLM-based synthetic tabular data work by Subah is incomplete; please provide a full citation and discuss its fairness evaluation more deeply to support the claimed gap in the literature.

Circularity Check

1 steps flagged · score 6.0 of 10

Central '<10% deviation' result is an artifact of the fairness-constrained generation loop used as its own evaluation.

fitted input called prediction [Section 2.4 (Synthetic Data Generation Framework) and Section 4 (Conclusion)]
"A constraint satisfaction module evaluates each batch for adherence to fairness and data fidelity requirements. If constraints are unmet, iterative prompt refinement and data adaptation steps are invoked. This loop continues until a final synthetic dataset Dsyn is obtained ... Our evaluation shows that the generated data closely mirrors real-world health data across fairness metrics, with deviations in Total Variation (TV), Direct Effect (DE), and Indirect Effect (IE) consistently below 10%."

The generation procedure repeatedly filters and adapts synthetic batches until 'fairness and data fidelity requirements' are met, and the paper's stated aim is to enforce causal fairness constraints. The evaluation then reports, as an independent finding, that the final synthetic data deviates by less than 10% from real data on TV, DE, and IE, which are exactly the causal fairness metrics being constrained. No held-out protocol, data split, or separate validation set is described, so the '<10% deviation' claim is a property fitted by the constraint-satisfaction loop rather than an out-of-sample prediction.

full rationale

The paper's causal framework (SCM, SFM, TV/DE/IE/SE definitions) is imported from Plecko et al. and other external references, and the FairAdapt predictor is also external; there is no load-bearing self-citation chain. The central circularity is in the evaluation design: Section 2.4 describes an iterative loop in which a constraint satisfaction module checks each synthetic batch for fairness and fidelity and continues refining until constraints are met, and Section 3.4.1 plus Section 4 report the resulting TV/DE/IE closeness as an evaluation result. Because the same family of causal fairness metrics appears both as the generation objective and as the evaluation criterion, and because no independent validation set or held-out protocol is specified, the '<10% deviation' claim reduces to the loop's own acceptance criterion rather than to a prediction from first principles. The 70% DE reduction claim is arithmetically consistent with Table 1 (real causally fair DE -0.0070 versus synthetic -0.0020), but it is not independent evidence for the same reason. Separately, Table 1 appears to contradict the specific '<10%' claim if deviations are computed relative to the real values: TV deviates by roughly 307% and IE by roughly 100%, with IE changing sign; however, that contradiction is a numerical correctness issue, not a circularity. Because at least one central evaluation claim is forced by construction, a score of 6 is appropriate; the remainder of the pipeline is not shown to be circular, and the causal-graph assumption, while potentially fragile, is an assumption rather than a circular step.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central evaluation rests on an unvalidated causal graph, the chosen LLM and constraint loop, and the FairAdapt model. None of these components is shipped or specified enough to audit, and the only numerical outcomes are fairness metric estimates, several of which contradict the headline claim.

free parameters (3)

Constraint thresholds for fairness and fidelity
The constraint satisfaction module in Section 2.4 accepts or rejects generated batches based on thresholds that are never stated. These thresholds control how closely the final synthetic data matches the real fairness metrics.
LLM generation settings
Model choice, prompt template, temperature, batch size, and iterative refinement schedule are not reported in Sections 2.4 and 3.3, but they determine the generated samples and all subsequent fairness values.
FairAdapt hyperparameters
The causally fair predictor uses FairAdapt in Section 3.3, but regularization or causal preprocessing settings are not reported, directly affecting the DE reduction claim.

assumptions (3)

domain assumption The Standard Fairness Model graph with sex (X), age (Z), clinical mediators (W), and survival (Y) is the true causal structure.
Section 3.2 assigns roles without structural equations or background justification. Every DE/IE/SE estimate depends on this graph.
standard math Path-specific causal effects are identifiable from the observational heart failure data under the cited estimator.
The paper applies Plecko et al. (2024a) formulas in Sections 2.3 and 3.3 and does not discuss hidden confounding, selection, or positivity conditions required for identifiability.
domain assumption A 299-row dataset is large enough for stable estimates of DE/IE/SE.
Standard deviations for TV and SE are comparable to the means, and the paper provides no power or sample-size analysis.

how reviews work

0 comments

Cite this review

Pith. "Pith review of FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation." pith.science (2026). https://pith.science/paper/T5RFBKWY

@misc{pith2026250619082,
  author       = {Pith},
  title        = {Pith review of: FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T5RFBKWY}},
  note         = {Machine review of arXiv:2506.19082}
}

read the original abstract

Synthetic data generation creates data based on real-world data using generative models. In health applications, generating high-quality data while maintaining fairness for sensitive attributes is essential for equitable outcomes. Existing GAN-based and LLM-based methods focus on counterfactual fairness and are primarily applied in finance and legal domains. Causal fairness provides a more comprehensive evaluation framework by preserving causal structure, but current synthetic data generation methods do not address it in health settings. To fill this gap, we develop the first LLM-augmented synthetic data generation method to enhance causal fairness using real-world tabular health data. Our generated data deviates by less than 10% from real data on causal fairness metrics. When trained on causally fair predictors, synthetic data reduces bias on the sensitive attribute by 70% compared to real data. This work improves access to fair synthetic data, supporting equitable health research and healthcare delivery.

Figures

Figures reproduced from arXiv: 2506.19082 by the authors.

**Figure 2.** FairCauseSyn generation and evaluation framework [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Comparison of causal fairness metrics - Total Variation (TV), Direct Effect (DE), Indirect Effect (IE), and [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

24 extracted references · 12 canonical work pages

[1]

Synthetic data--what, why and how? arXiv preprint arXiv:2205.03257, 2022

James Jordon, Lukasz Szpruch, Florimond Houssiau, Mirko Bottarelli, Giovanni Cherubin, Carsten Maple, Samuel N Cohen, and Adrian Weller. Synthetic data--what, why and how? arXiv preprint arXiv:2205.03257, 2022

arXiv 2022
[2]

A survey on bias and fairness in machine learning

Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. A survey on bias and fairness in machine learning. ACM computing surveys (CSUR), 54 0 (6): 0 1--35, 2021

2021
[3]

The problem of fairness in synthetic healthcare data

Karan Bhanot, Miao Qi, John S Erickson, Isabelle Guyon, and Kristin P Bennett. The problem of fairness in synthetic healthcare data. Entropy, 23 0 (9): 0 1165, 2021

work page 2021
[4]

Modeling tabular data using conditional gan

Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. Modeling tabular data using conditional gan. Advances in neural information processing systems, 32, 2019 a

work page 2019
[5]

Tabddpm: Modelling tabular data with diffusion models

Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, and Artem Babenko. Tabddpm: Modelling tabular data with diffusion models. In International Conference on Machine Learning, pages 17564--17579. PMLR, 2023

2023
[6]

Realtabformer: Generating realistic relational and tabular data using transformers

Aivin V Solatorio and Olivier Dupriez. Realtabformer: Generating realistic relational and tabular data using transformers. arXiv preprint arXiv:2302.02041, 2023

arXiv 2023
[7]

Language models are realistic tabular data generators

Vadim Borisov, Kathrin Se ler, Tobias Leemann, Martin Pawelczyk, and Gjergji Kasneci. Language models are realistic tabular data generators. arXiv preprint arXiv:2210.06280, 2022

arXiv 2022
[8]

Tabfairgan: Fair tabular data generation with generative adversarial networks

Amirarsalan Rajabi and Ozlem Ozmen Garibay. Tabfairgan: Fair tabular data generation with generative adversarial networks. Machine Learning and Knowledge Extraction, 4 0 (2): 0 488--501, 2022

work page 2022

Show all 24 references

[9]

Counterfactual fairness

Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. Counterfactual fairness. Advances in neural information processing systems, 30, 2017

2017
[10]

Path-specific counterfactual fairness

Silvia Chiappa. Path-specific counterfactual fairness. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 7801--7808, 2019

2019
[11]

Fairgan: Fairness-aware generative adversarial networks

Depeng Xu, Shuhan Yuan, Lu Zhang, and Xintao Wu. Fairgan: Fairness-aware generative adversarial networks. In 2018 IEEE international conference on big data (big data), pages 570--575. IEEE, 2018

2018
[12]

Achieving causal fairness through generative adversarial networks

Depeng Xu, Yongkai Wu, Shuhan Yuan, Lu Zhang, and Xintao Wu. Achieving causal fairness through generative adversarial networks. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019 b

2019
[13]

Fairness in decision-making—the causal explanation formula

Junzhe Zhang and Elias Bareinboim. Fairness in decision-making—the causal explanation formula. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018

2018
[14]

Learning optimal fair policies

Razieh Nabi, Daniel Malinsky, and Ilya Shpitser. Learning optimal fair policies. In International Conference on Machine Learning, pages 4674--4682. PMLR, 2019

2019
[15]

Causal fairness analysis: a causal toolkit for fair machine learning

Drago Ple c ko, Elias Bareinboim, et al. Causal fairness analysis: a causal toolkit for fair machine learning. Foundations and Trends in Machine Learning , 17 0 (3): 0 304--589, 2024 a

2024
[16]

Causal fairness under unobserved confounding: a neural sensitivity framework

Maresa Schr \"o der, Dennis Frauen, and Stefan Feuerriegel. Causal fairness under unobserved confounding: a neural sensitivity framework. arXiv preprint arXiv:2311.18460, 2023

2023 arXiv
[17]

Decaf: Generating fair synthetic data using causally-aware generative networks

Boris Van Breugel, Trent Kyono, Jeroen Berrevoets, and Mihaela Van der Schaar. Decaf: Generating fair synthetic data using causally-aware generative networks. Advances in Neural Information Processing Systems, 34: 0 22221--22233, 2021

2021
[18]

Mitigating and assessing bias and fairness in large language model-generated synthetic tabular data

Faria Zarin Subah. Mitigating and assessing bias and fairness in large language model-generated synthetic tabular data
[19]

Self-consistency improves chain of thought reasoning in language models

Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171, 2022

2022 arXiv
[20]

A survey on in-context learning

Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, et al. A survey on in-context learning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 1107--1128, 2024

2024
[21]

Sgp-tod: Building task bots effortlessly via schema-guided llm prompting

Xiaoying Zhang, Baolin Peng, Kun Li, Jingyan Zhou, and Helen Meng. Sgp-tod: Building task bots effortlessly via schema-guided llm prompting. arXiv preprint arXiv:2305.09067, 2023

2023 arXiv
[22]

fairadapt: Causal reasoning for fair data preprocessing

Drago Ple c ko, Nicolas Bennett, and Nicolai Meinshausen. fairadapt: Causal reasoning for fair data preprocessing. Journal of Statistical Software, 110: 0 1--35, 2024 b

2024
[23]

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Davide Chicco and Giuseppe Jurman. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC medical informatics and decision making, 20: 0 1--16, 2020

2020
[24]

Advancing Ethical and Responsible AI: Exploring Fairness, Privacy, and Explainability through Causal Perspectives

Karima Makhlouf. Advancing Ethical and Responsible AI: Exploring Fairness, Privacy, and Explainability through Causal Perspectives. PhD thesis, \'E cole polytechnique, 2024

2024

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

Synthetic data--what, why and how? arXiv preprint arXiv:2205.03257, 2022

James Jordon, Lukasz Szpruch, Florimond Houssiau, Mirko Bottarelli, Giovanni Cherubin, Carsten Maple, Samuel N Cohen, and Adrian Weller. Synthetic data--what, why and how? arXiv preprint arXiv:2205.03257, 2022

arXiv 2022

[2] [2]

A survey on bias and fairness in machine learning

Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. A survey on bias and fairness in machine learning. ACM computing surveys (CSUR), 54 0 (6): 0 1--35, 2021

2021

[3] [3]

The problem of fairness in synthetic healthcare data

Karan Bhanot, Miao Qi, John S Erickson, Isabelle Guyon, and Kristin P Bennett. The problem of fairness in synthetic healthcare data. Entropy, 23 0 (9): 0 1165, 2021

work page 2021

[4] [4]

Modeling tabular data using conditional gan

Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. Modeling tabular data using conditional gan. Advances in neural information processing systems, 32, 2019 a

work page 2019

[5] [5]

Tabddpm: Modelling tabular data with diffusion models

Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, and Artem Babenko. Tabddpm: Modelling tabular data with diffusion models. In International Conference on Machine Learning, pages 17564--17579. PMLR, 2023

2023

[6] [6]

Realtabformer: Generating realistic relational and tabular data using transformers

Aivin V Solatorio and Olivier Dupriez. Realtabformer: Generating realistic relational and tabular data using transformers. arXiv preprint arXiv:2302.02041, 2023

arXiv 2023

[7] [7]

Language models are realistic tabular data generators

Vadim Borisov, Kathrin Se ler, Tobias Leemann, Martin Pawelczyk, and Gjergji Kasneci. Language models are realistic tabular data generators. arXiv preprint arXiv:2210.06280, 2022

arXiv 2022

[8] [8]

Tabfairgan: Fair tabular data generation with generative adversarial networks

Amirarsalan Rajabi and Ozlem Ozmen Garibay. Tabfairgan: Fair tabular data generation with generative adversarial networks. Machine Learning and Knowledge Extraction, 4 0 (2): 0 488--501, 2022

work page 2022