Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper proposed a new model for causal proxy learning for average dose-response function estimation of multidimensional treatments.  This is done by based on combining contrastive regularizer to learn the proxy representation, and ranked weighting method to de-bias the treatment assignment mechanism estimation. The authors claims that the proposed method can avoid the deficiencies of existing methods that rely on correctly specified treatment assignment models and/or require strong prior of the unobserved confounder distribution. 
strength_and_weaknesses: **Strength**

- The proposed method is new to the best of my knowledge. Under the proxy assumption, the paper proposes to combine contrastive learning of the proxies and the rank weight estimations of the propensity scores to estimate treatment effects.
- The paper has relatively extensive set of baselines and plenty of tasks in the experimental analysis part. I also appreciate that the authors have detailed all synthetic functions in the appendix for reproducibility. 

**Weakness**
- Missing important references and lacks clear motivation.  The problem setting of this paper is very closely related to (Yixin, 2021), which should definitely be mentioned and compared. I found it hard to be convinced by the author's argument that "existing methods rely on correctly specified treatment assignment models and/or require strong prior of the unobserved confounder distribution", since the identification results in (Wang, 2021) does not rely on any of these. Given those, the paper lacks key motivations for the Cr + Rw approach.

- The claim that "our method need no data distribution prior and outperforms others in performance" is not very convincing. To some degree, the choice of similarity functions in the contrastive learning part already implicitly assumes certain prior preferences. Even assuming it's true that this method requires absolutely no prior, this comes at a cost of: 1), one needs to specify the similarity function as well; 2), the identifiability of both treatment effects and latent confounders become unclear. 

- Although the proposed method is new, I don't immediately see the key novelty of the paper. The contrastive learning is not a new method and has been used for learning latent representations for a long time; the rank weighting might have certain degree of novelty, however I don't see how this relates to the proxy causal learning setting. It looks more like a standalone estimator on this own and needs more theoretical analysis and experiment evaluation for justification.  
- Lacks rigorous theoretical analysis. For example, the authors claim that " learned representation E(X) refuse the information of $\epsilon_1$ and maintain the information of $U$, which means we eliminate the error item...", which is too heuristic and needs to be formally derived. Also, the paper lacks identifiability analysis for $U$ (or treatment effects). Without those analysis, Eq(11) that the authors proposes hardly qualifies as an "identification"; and simply dodging this issue by hand-waving " The performance of using proxy to estimate ADRF depends on the degree of recovery of $U$" is clearly unconvincing. 

**Minor issues**

- In related works, the paper cited a bunch of methodologies and claims that the proposed method "outperforms others in performance". Unfortunately, many of those mentioned in this part are not used in experiments at all. The authors need to be careful on what their claims and avoid over-selling.

- The writing of the paper is not very consistent. For some parts it is very clear and convincing, but for some other parts it is very confusing and I found it hard to understand what the authors are trying to do. I finally managed to understand most of it, but at the cost of reading it multiple times and making my own guess on a number of undefined terms and notations. 


**Reference**

Wang, Yixin, and David Blei. "A proxy variable view of shared confounding." International Conference on Machine Learning. PMLR, 2021.
clarity,_quality,_novelty_and_reproducibility: See above.
summary_of_the_review: Causal proxy learning is generally a very interesting topic, and this paper proposes a new solution to it. However, the motivation for the design choices of the paper is quite unclear; and the theoretical justification is not very convincing.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper proposes the use of contrastive learning to deconfound the observed proxy variable of the confounder and treatment for unbiased continuous treatment effect estimation. In addition, some weighting methods are incorporated for score balancing. The proposed method has been evaluated using synthetic and sem-synthetic datasets. It shows that the use of contrastive learning approach can achieve the treatment effect estimation without the need to model the confounders expicitly.
strength_and_weaknesses: Strengths:
+ Unbiased continuous treatment effect estimation is an interesting and important problem.
+ The idea of using contrastive learning for learning the representation of observed proxy variables and the treatments provides an interesting perspective to address the problem as compared to the existing VAE and GAN approaches.

Weaknesses:
- The key contribution is the use of the Cr regularizer. The novelty related to the weighting for the score balancing is considered limited.
- Ablation study is missing. So, it is not clear about the relative importance of Cr and Rw.
- Only CEVAE was compared in the experiment while there are other more recent VAE models proposed for the same application (e.g., intact-VAE). Whether the proposed CRNet can perform the more recent extensions of CEVAE is unclear.
- The proposed approach may not be useful for applications where the cofounders are intended to be modeled explicitly.
- The evaluation is only carried out using synthetic and semi-synthetic data, and no real data is used.
- There are quite many typos and presentation issues in the paper as listed in the section of the review.

clarity,_quality,_novelty_and_reproducibility: The perspective and particular way of using contrastive learning to achieve unbiased treatment effect estimation, to the best of my knowledge, carries novelty.

The readability of the paper is generally ok for readers with some background, and could be further improved. The argument of having the advantage of not requiring the prior is briefly mentioned in Section 1 and seems not much discussed after Section 1. 

There are quite many typos and presentation issues in the paper. The following shows some spotted while I am reading the paper
p2 line 12: And with an **ideally** representation for confounders, we would adopt a balancing **methods**
p3 Definition 2: \Phi^* is not defined
p3 Section 3.2: "And the **the** estimated ..."
p3 Section 3.2: "Then we **the** divide ADRF estimation with "
p5 line 6: "these two responsibilities **complete** each other.
p5 line 6/7: " learned representation E(X) **refuse** the .."
p7 Eq 10: l_{Er} should be i_{Cr}
summary_of_the_review: The paper addressed an interesting and important problem. The perspective of using contrastive learning to solve the unbiased treatment effect estimation is an interesting one and carries some novelty. The performance comparison with more recent variants of CEVAE is not included. It is hard to evaluate the significance of this work.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: The paper tackles the problem of estimating the effect of continuous and multi-dimensional treatments in the setting with unobserved confounders. The paper assumes access to high dimensional proxy variables for the unobserved confounders and proposes a Contrastive regulariser which learns a proxy representation for the unobserved confounders. The proposed method, CRNet, uses the Contrastive regulariser, together with a Rank weighting methods which de-biases the treatment assignment, to effectively estimate the effects of multiple continuous treatments. 
strength_and_weaknesses: Strengths:
-  The paper addresses an important and understudied problem in the causal inference literature, which is the estimation of treatment effects for continuous and multi-dimensional treatments in the challenging setting of unobserved confounders
- The use of contrastive learning to learn the proxy regularisation + the rank weighting to address the selection bias is interesting and aims to overcome the known problems of balanced representations which may result in too strong regularisation and discard information about the confounders
- CRNet is thoroughly evaluated on many datasets and demonstrates performance improvements over the benchmarks. 


Weaknesses:
- The authors simplify the Average Dose-Response Function estimation problem through the additive regression model in equation (2). I think the paper would benefit from a more detailed discussion regarding how realistic this simplification is and in which cases it holds.
- The paper makes many assumptions; while I believe that this is needed to be able to handle the setting of unobserved confounders, the papers should include a more detailed discussion about the implications of these assumptions and how realistic they are in practice. 
- It it unclear how learning the representation f(X) through contrastive learning helps with recovering the hidden confounders. In particular, why does strengthening the association between treatments T and covariates X helps to recover the unmeasured common cause U? I believe the authors should describe this in more detail, and if possible, develop theory to support this claim which I believe is crucial for the paper. 
- To approximate the treatment score $\mathbb{P}(\mathbf{T} \mid \mathbf{U})$ the paper assumes it follows a Gaussian mixture model and uses a mixture density network to learn it. I believe that this assumption about the distribution of the treatments given the unobserved confounders should be more explicitly acknowledged and again discussed in which setting it is valid. 


clarity,_quality,_novelty_and_reproducibility: Related work:
- The paper is missing relevant related works that also handle the case of hidden confounders for treatment effects estimation [1-3]

Reproducibility:
- The authors provide detailed descriptions of the datasets used for evaluation and the hyper parameters used by the model which I believe helps with reproduction. 

Minor clarity issues. 
- I believe that the clarity of the paper would improve if they authors mentioned what the expectations in Section 3.2 are computed over. 
- The citation for Proxy (Guo et al., 2020) in Section 2 might be incorrect as the referenced paper is a survey paper.
- Typo “encode X representations”
- Typo in equation (10) where it should be $l_{Cr}$ instead of $L_{Er}$


[1] Wang, Yixin, and David Blei. "A proxy variable view of shared confounding." International Conference on Machine Learning. PMLR, 2021.
[2] Wang, Yixin, and David M. Blei. "The blessings of multiple causes." Journal of the American Statistical Association 114.528 (2019): 1574-1596.
[3] Mayer, Imke, et al. "Missdeepcausal: Causal inference from incomplete data using deep latent variable models." arXiv preprint arXiv:2002.10837 (2020).


summary_of_the_review: Overall, I believe that the method developed in this paper, CRNet aims to address an important and understudied problem in the causal inference literature and achieves good performance in practice. However, I believe the authors should include more details regarding how the learnt representations recover the hidden confounders, as this is a crucial point for the paper. Moreover, the paper makes many explicit and implicit assumptions, whose implications are not discussed.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: Estimating treatment effect in the presence of unobserved confounders is a challenging but omnipresent problem. The authors assume availability of a proxy for unobserved confounders and attempt to estimate it to then infer weights that can be used for unbiased treatment effect estimation. The main contribution of the paper is the proposal of "rank weights" for this purpose.
strength_and_weaknesses: **Strengths**
- The paper proposes new ideas to leverage the proxy model and show that the proposed approach outperforms in several example.
- The experiments consider many different baselines.

**Weaknesses**
- The paper has serious flaws in presentation that hinder the comprehension of the method, assumptions, and novelty. For instance, in the preliminaries section, a potential outcome is never defined, $\phi$ is not defined, the notation $\phi(\cdot | t)$ is not defined, Def. 2 and the notation do(t) independent of U are highly non standard and should be explained. Given this I don't understand the motivation section of the paper.
- Assumptions 5 and 6 should be discussed in more detail. It is not clear how plausible they are. Especially, assumption 6 seems to directly contradict the causal graph in Fig. 1.
- To my knowledge none of the semi-synthetic datasets incorporate a proxy structure in their data generating mechanisms. Are these changed to fit the setting in this paper. Do you loose performance when the correct thing to do is simply condition on observed covariates, i.e. if there are no unobserved confounders?

**Suggestions** 
- One reference on ITE with continuous treatment could be added: Bellot, Alexis, Anish Dhir, and Giulia Prando. "Generalization bounds and algorithms for estimating conditional average treatment effect of dosage." arXiv preprint arXiv:2205.14692 (2022).
clarity,_quality,_novelty_and_reproducibility: I believe there are serious issues in the presentation that should be addressed. I didn't find this work to give a particularly novel insight into the problem in the context of related work.
summary_of_the_review: My recommendation is to reject. I do believe that there are promising ideas and I am open to revise my score but at this moment there are many imprecisions that have to be corrected.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: This paper proposes the use of contrastive learning to deconfound the observed proxy variable of the effect estimation. 

strength

+ The paper proposes new ideas to leverage the proxy model and show that the proposed approach outperforms in several examples.

+ The use of contrastive learning to learn the proxy regularisation + the rank weighting is interesting. It could potentially overcome the known problems of balanced representations which may result in too strong regularisation and discard information about the confounders

weakness

+ The paper needs to discuss in more detail the assumptions, esp assumptions 5 and 6. 

+ The paper needs to provide a more rigorous justification of the proposed method (esp on the identifiability theory) and more thorough empirical validation. The evaluation is only carried out using synthetic and semi-synthetic data, and no real data is used.

+ The current framing of the paper does not convey clear motivation nor reveal key novelty of the paper.

The paper is not ready for publication in its current form. We encourage the authors to make revisions and submit to a future venue.

justification_for_why_not_higher_score: The paper still needs to discuss in more detail the assumptions made and provide a more rigorous justification of the proposed method.
Decision: Reject