Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper proposes a GAN based causal aware generative model to generate fair data for downstream models. They provide theoretical motivation and practical solutions with different causal criteria. 
main_review: Pros:
* This paper considers using a causal aware generative model to generate fair datasets, which I believe is the right approach to handle this problem. 
* The paper is presented clearly
* The paper provides a theory to motivate the proposed solution 
* The paper has a meaningful discussion regarding how the method can be used in practice (tool for discovery and markove compatible) 

Cons:
* Concerns regarding stability of the method (2 below)
* Concerns about biased data leads to wrong DAG, which cannot generate fair data (point 5 below)

5 is my main concern below comparing to others, and I am looking forward to your reply and happy to change the score if 5 can be addressed. 

Details: 

1. You seem need to assume to know the underlying downstream task. If the task selects a different variable as a target, the fairness guarantee won't be there anymore, right? Do you have any thoughts on generating fair datasets generation? 
2. I have concerns about the scalability of the method. If it is tabular data with many variables and with many edges, how would you implement it? ALso, do you need to write a new piece of code given any new causal graph or do you automate it in some way? If so, how?
3. Causal graph is known is a strong assumption. But it is fine as you discussed discovery methods. In this case, do you have results for your method with respect to error from causal discovery?
4. line 208. You mentioned Markov compatible and in experiments, you used Tetrad. I assume that the output from the Tetrad with score-based method will be a CPDAG. How did you use CPDAG to design the generator? Did you just take one sample from the Markov equivalent class. How robust is your method to different DAG in the Markov equivalent class? Does these make a difference in experiments?
5. I believe that causal sufficiency is an assumption here.  However, bias in the dataset itself may be due to selection bias. And existing causal discovery methods have problems to hand selection bias. Thus, the root of the bias cannot to resolved. (the root cause of unfair also it the root cause of the failure of causal discovery methods. ) For example, in 20s the survey about IQ, gender etc where there is fewer females in university where the survey is taken and leads to wrong conclusions. Such selection bias makes causal discovery fail and also makes other ML methods unfair. [Check causal discovery for MNAR papers]
6. From the generative modeling point of view, the model seems just the GAN version of the CAMA model Zhang C, Zhang K, Li Y. A Causal View on Robustness of Neural Networks. Advances in Neural Information Processing Systems. 2020;33. 
limitations_and_societal_impact: This paper is on the topic of responsible AI and if successful. It brings a more positive impact to machine learning applications. There is no explicit discussion regarding the potential negative societal impact. But I do encourage the authors to discuss more if people rely on their method (which may contain error from causal graph or model training), how to communicate to people about the results, and fairness guarantees. Also, consider how to communicate the assumptions to users to make sure that the methods are used correctly.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This work proposes a novel task of generating fair synthetic data by a GAN-based model. The generation process follows the order on a causal DAG and fairness is achieved by debiasing during the inference phase. Experiment results are carried out on Adult and Credit Approval datasets to show the effectiveness of generating fair synthetic data. 

main_review: Originality: 
- Generating fair synthetic data for machine learning is an interesting task. This paper designed a GAN-based framework which can achieve fair data generation by simply intervening the inference phase. The idea is novel and interesting. 

Quality:
- Some discussion on efficiency: Although the idea of generating synthetic data following a topological order is an interesting idea, it also raises a concern about the efficiency of the algorithm. The algorithm needs to run the generator d times, where d is the number of columns in the table. If there are only a few protected columns, it’s possible to simplify the DAG by merging irrelevant columns. However, if the number of protected columns are also large, then there’s no easy fix to the efficiency. 
- Comparing with more baseline methods: The method is only compared with GAN-based baselines. Since statistical methods are also capable of generating synthetic data, such baselines should also be included, for example PrivBayes [1]. Also, there are GANs (TableGAN [2], CTGAN [3]) and VAEs (TVAE [3]) designed specifically for synthetic table generation. 
- The experiment is carried out on 2 small dataset, each with less than 20 columns. It’s unclear how the model would perform on a dataset with more columns.  

Clarity
- This paper clearly defines different fairness in synthetic data. The high-level idea is also understandable.
- However, I would ask the authors to precisely describe the GAN model using either pseudo code or equations in the main paper, rather than burying all details deeply in the supplementary material and even in the source code. For example, the sharing weights in D.1 is important and quite hard to understand. It’s also unclear how the paper handles categorical (discrete) columns in tabular data. 
- I also recommend the authors also provide stats for fair credit dataset. 

Significance: 
- This paper clearly lays out different definitions of fair synthetic data, and the proposed method is novel and interesting. These ideas are inspiring for future research. 

[1] PrivBayes: Private Data Release via Bayesian Networks
[2] Data Synthesis based on Generative Adversarial Networks
[3] Modeling Tabular data using Conditional GAN

limitations_and_societal_impact: I don’t think this work can lead to any negative social impact.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This work tackles the problem of fairness from a pre-processing perspective by handling the data first using some form of generative transformation. In particular, they use a GAN as this generation and embed a causal structure within the layers. There are then theoretical guarantees presented on the convergence and guarantees of fairness when using the processed data on other tasks.
main_review: The goal is the submission is to make work towards handling bias in machine learning systems but in particular take a pre-processing approach to this. Overall, such an approach has modularity since any high-performing or even biased algorithm can then use this method to produce fair ML systems, attesting to the significance of this approach.

The major strength of this submission is precisely this method due to its modularity, which as illustrated seems to be a practical method for practitioners. The weakness I see in this work are the theoretical guarantees, which seem to be quite implicit and lacking - for example, can the authors provide some kind of convergence rates as opposed to just guarantees? On first reading of 'convergence guarantees of the generator', I expected some form of distributional convergence or functional convergence (since they mention the generator will converge) so it would be better if the authors can be more candid about this contribution in the abstract. However, I do not think this is such a major concern. 

I am not an expert in this area however the proposal of a GAN-based method that embeds causal structure seems to be quite original and the relevant work seems to be discussed. The paper is well-written with the relevant notation being self-contained and motivation spelled out clearly. I briefly checked the proofs and think the quality of correctness makes sense.

Overall, I rate this paper above the threshold due to the modularity of the approach however since it is not my expertise I cannot comment entirely on the novelty. I think this work is highly relevant to the NeurIPS community and certain can benefit practitioners. One question I had for the authors to better understand this work is: How would this method perform if you trained a GAN on the pre-processed data to create larger datasets?
limitations_and_societal_impact: Yes they have.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper proposes DECAF, a fair data generation method using causal DAG structures and GANs. The DAG is used to remove any causal dependencies that may have a negative effect on the fairness and is general enough to model various fairness measures: FTU, DP, and CF. Then synthetic data is generated according to the DAG, but made realistic using a discriminator that distinguishes the generated samples from the real ones. Experiments show that DECAF has comparable fairness to the state-of-the-art method FairGAN, but significantly outperforms it in terms of precision, recall, and AUROC. 
main_review: This paper proposes a novel approach for removing data biases that negatively affect fairness, yet generating realistic data using GAN techniques. There are some concerns on whether the generated data is realistic and whether DECAF should also be compared with weak supervision approaches.

Strong points:

* The unfairness mitigation method is principled and has theoretical guarantees. By removing edges from the DAG, one has a clear idea exactly what bias is being removed.

* The DAG representation is general and can be used to express prominent group fairness measures.

* The experiments show that DECAF outperforms the state-of-the-art method FairGAN among others.

Weak points:

* One concern is whether removing edges is too drastic and may result in unrealistic data. For example, Sec 5.2 mentions that surrogate variables can be sampled independently from a distribution or set to a fixed value. Let us say that we are using the Adult dataset and set all its gender values to male. From a practical point of view, wouldn't the dataset lose too much information? To answer this question, the authors should also show DECAF's discriminator performance because it may actually be able to distinguish generated versus real data better than say FairGAN's real/fake discriminator.

* An alternative approach for fair data generation is to use weak supervision. In particular, the authors should cite and compare DECAF with Choi et al., "Fair Generative Modeling via Weak Supervision", ICML 2020. The idea is to exploit an additional small, unlabeled reference dataset as the supervision signal to generate unbiased data.

* Some notions are used without definitions. In Sec. 3, please define "independent causal mechanism". In Sec. 4.3, define "Markov boundary", "d-separation", and "explanatory feature".

* The experiments section looks a bit thin (accuracy and fairness experiments for two datasets), probably because many experiments are moved to the supplementary. In particular, the Adult dataset DAG is quite interesting and should be more visible. Also, it would be useful to see an ablation study that evaluates DECAF without some of its components (e.g., the discriminator). 

* In Sec. 6, please specify how the values of surrogate variables were chosen. There are some details in the supplementary, but it is not clear if the same settings were used in Sec. 6.
 
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The author feedback addresses my concerns. I will keep my rating of 6. 
limitations_and_societal_impact: Yes. Section 7 has a detailed discussion on limitations and societal impact.
review_title: null

Meta-review: While the paper has initially some disagreement in score, it now arrives at a unanimous consensus by properly addressing all of the main concerns, in particular, regarding the limitation of the underlying assumption made in the paper, as well as providing more experimental results w.r.t. suggested baselines. I believe the paper is worth being published as a poster.
Decision: Accept (Poster)