Debiasing Synthetic Data Generated by Deep Generative Models

Summary

The authors propose a method for debiasing DGM-generated tabular data such that the population average of the EIC for the observed data distribution and its estimation becomes zero, resulting in elimination of the corresponding biasing term. This is done through augmenting the DGM output such that the population mean of the synthetic data matches the sample average of the observed data. As such, the method is generator-agnostic. The authors validate their approach through extensive experiments, demonstrating significant reductions in bias while maintaining data utility.

Strengths

1. The paper is technically sound with in-depth theoretical analysis that are backed up by practical examples. We appreciate the additional detailed investigations presented in the Appendix, which shows the dedication of the authors to do a robust evaluation of their methodology. 2. The paper tackles the critical issue of bias in synthetic data, a relatively underexplored area. The proposed method integrates fairness constraints directly into the data generation process, a novel approach, as opposed to post-hoc fairness adjustments. The proposed methodology specifically addresses the bias term concerning the observed data distribution, O, and its DGM estimation, P, as opposed to previous efforts that (are said to) have focused on the bias term describing the discrepancy between the sampled estimated (synthetic) distribution, S, and the approximation of P as P¨ (P-hat) through S. 3. The paper is very well written and well organized (it was a pleasure to read). Figures and tables are well-presented and support the narrative effectively. 4. The results have significant implications for improving fairness in machine learning, an area of growing importance. The proposed method for debiasing synthetic tabular data is straightforward and well described and hence easily implemented and adaptable by others

Weaknesses

1. The paper would benefit from a more in-depth evaluation of potential limitations or edge cases where the method might not perform as well. 2. It is difficult to assess the novelty in the proposed method as there is no presentation of related works nor comparison with alternative methods. A more detailed review of related work would help contextualize the novelty of the proposed method. 3. It would be good to put the work in contrast with related works would improve the paper and elucidate the authors contribution to the field. 4. The significance is still difficult to assess due to the aforementioned lack of comparison to alternative approaches. Additionally, potential impacts on different domains (e.g., healthcare, finance) could be explored to highlight the broader significance.

Questions

1. Could the authors provide more details on how their approach scales with larger datasets and more complex models? 2. How does the proposed method perform in scenarios with multiple sources of bias or more intricate data dependencies? 3. Can the authors elaborate on any potential trade-offs between debiasing effectiveness and computational efficiency? 4. How is "a very large sample" defined? L146 says 1,000,000 observations is "large", but the quality analysis in Section A.7.2 has max n = 5,000. 5. Is the necessary synthetic data sample size generalizable or does it depend on the complexity of the parameter space?

Rating

7

Confidence

3

Soundness

3

Presentation

3

Contribution

3

Limitations

The authors properly address limitations of their work. However, they could further discuss the scalability of the proposed method to large datasets.

Summary

This paper addresses the significant biases introduced in synthetic data generated by deep generative models (DGMs) that compromise the inferential utility of such data. The authors propose a new debiasing strategy based on techniques adapted from debiased or targeted machine learning. Their approach aims to reduce biases and enhance convergence rates, thereby improving the reliability of statistical analyses performed on synthetic data. The effectiveness of the proposed strategy is demonstrated through a simulation study and two case studies using real-world data.

Strengths

- **Originality:** The paper tackles the important problem of bias in synthetic data generated by DGMs, which has significant implications for privacy protection and data analysis. The authors propose a novel debiasing strategy that adapts techniques from targeted machine learning to the context of synthetic data generation. This represents a new direction in addressing the challenges of inferential utility in synthetic data. The approach is generator-agnostic, making it widely applicable to various types of DGMs. - **Quality:** The submission is technically sound, with clear explanations of the theoretical foundations and derivations of the debiasing strategy. The authors provide a simulation study and two case studies that demonstrate the effectiveness of their approach in improving the coverage of confidence intervals and enabling more reliable statistical inference from synthetic data. The paper is well-written and organized, making it easy to follow the authors' reasoning and understand their contributions. - **Significance:** The results presented in the paper are relevant for the field of synthetic data generation and privacy protection. The proposed debiasing strategy has the potential to improve the reliability and applicability of synthetic data in statistical inference, which can have broad implications for various domains where privacy is a concern.

Weaknesses

- **Limited Scope:** The simulation study and case studies focus on low-dimensional settings and a limited number of estimators (population mean and linear regression coefficient). While the positive results are encouraging, further evaluation is needed to assess the effectiveness of the debiasing strategy in high-dimensional settings and for a wider range of statistical analyses. - **Dependence on EICs:** The proposed debiasing strategy requires access to the Efficient Influence Curves (EICs) of the target parameters of interest. This limits the applicability of the method to parameters that are pathwise differentiable and have known EICs. - **Conditional Sampling Limitation:** The debiasing procedure for the linear regression coefficient requires sampling synthetic data conditional on covariates, which may not be feasible for all types of DGMs.

Questions

- **High-Dimensional Settings:** How well does the debiasing strategy perform in high-dimensional settings where DGMs are more commonly used? Are there any challenges or limitations in applying the method to high-dimensional data? - **Complex Estimators:** Can the debiasing strategy be extended to more complex estimators beyond the population mean and linear regression coefficient? - **Alternative Debiasing Approaches:** Are there alternative debiasing approaches that could be explored to address the limitations of the proposed method, such as its dependence on EICs and the conditional sampling requirement? - **Privacy Considerations:** While the paper mentions the potential increase in privacy disclosure risk with larger synthetic datasets, it would be beneficial to discuss this trade-off in more detail. How can the debiasing strategy be balanced with privacy concerns, especially in the context of differentially private synthetic data generation? - **Failure of Assumptions**: Are there specific scenarios where the assumptions required for the debiasing method might not hold, and how would this impact the results?

Rating

7

Confidence

1

Soundness

3

Presentation

3

Contribution

3

Limitations

The authors themselves are highly aware and forthcoming about the limitations of this study, which in my eyes is a big bonus. - The authors discuss the low-dimensional setting of their simulations and acknowledge that DGMs might be less suited for such settings. - They note the need for the person generating synthetic data to be aware of the analyses that will be run, which may limit the method's applicability. - The authors note the need to condition on data covariates to generate appropriate synthetic data. - The potential trade-off between synthetic data sample size and privacy risks is mentioned, though it is beyond the scope of this paper.

Summary

This paper tackle the problem of creating/imputing unbiased synthetic data, and analyse the potential bias brought by these imputation methods.

Strengths

The tackled problem is important and very timely, and linked to the general question of biais of generative models [1]. [1] Wyllie, Sierra, Ilia Shumailov, and Nicolas Papernot. "Fairness feedback loops: training on synthetic data amplifies bias." The 2024 ACM Conference on Fairness, Accountability, and Transparency. 2024.

Weaknesses

I do not know if this comes from my lack of knowledge in the field, but I found the paper very hard to understand, no clear theoretical result is encapsulated, and it is not even very clear to me what is proposed (I guess this is what is after line 91). I thus have a lot of questions: - line 90, how exactly $S_i$ is sampled? Are you sampling first $A_i$ and $X_i$, and then $Y_i|A_i, X_i$? - What is your main theoretical result? Is it Equation 2? or Section 3.3? - What is $\phi$ (I am not sure it is defined in the main text) - How is the deep generative model learned in the first place?

Questions

see above

Rating

4

Confidence

1

Soundness

2

Presentation

1

Contribution

2

Limitations

yes

Summary

The paper considers the problem of debiasing synthetic data which can have signficant issues when used for statistical analysis. In particular, they show examples of mean estimation of a variable and parameter estimate of a regression model and demonstrate the benefits of the post-processing step of their approach (model agnostic). Overall: The paper is clearly written and the problem is well-defined. The approach is for debiasing is simple but effective and experiments validate the approach. Unclear, how it can work for more real-world settings.

Strengths

(i) The problem of bias in the synthetic data is well explained by considering the two examples of population mean and the coefficient estimator in linear regression. The debiased estimators are derived for these two settings. (ii) Experiments are first shown the synthetic datasets and the debiased estimators have very good coverage as opposed to without debiasing. The convergence rate with O(1/sqrt(n)) is al so shown when debiased. Two real-world datasets are also considered and the issue of obtaining false conclusions from the synthetic data vs the debiased setting is illustrated.

Weaknesses

(a) As mentioned in the discussion settings, the datasets are low-dimensional and the estimators are very simple such as mean and parameter estimate for linear regression. (b) It is unclear how one can generalize these to settings where the estimators are much more complex/ubiquitous such as the covariance matrix or group fairness.

Questions

(1) The references are a bit sparse on the synthetic data literature and would it be possible to enhance it with appropriate hooks? There are quite a few surveys available now. (2) While the results are interesting, it is unclear how much is innovative in this work as opposed to building on the results from Chernozhukov, Decruyenaere and other cited works. Can you elucidate that clearly? (3) Also, what would be needed to handle more interesting estimators for downstream applications?

Rating

5

Confidence

4

Soundness

3

Presentation

3

Contribution

2

Limitations

NA

Response

I thank the authors for their detailed and thorough response. I will keep my original score.