Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: The paper aims at learning the causal representation from time-series data with the access of interventions. The formulation is a state-space model, which the causal process is unobserved and the observation is a funtion of the unobserved causal process. It generalizes CITRIS (Lippe et al., 2022b) to include instantaneous effects in the underlying causal graph. To identify the unique representation, the paper requires the access to the intervensions on each latent variable. The causal graph and the causal representation (or variables) can be identified with the access of the interventions by maximizing likelihood, maximizing the information content, and minimizing the edges of a causal graph.

strength_and_weaknesses: The strength:

(+) The work spends a lot of effort on the proof of identifiability.

(+) The problem is important: the generalization of CITRIS (Lippe et al., 2022b) in the presence of instantaneous effects.


(-) The identifiability relies on the access to the interventions on each causal variable, which can not be easy to get in practice. This in turn limits the contribution of the work by the setting of the solution.

(-) The paper claims to identify the causal representation; however, the experiments didn't show what is the learned causal representation. Perhaps, besides showing the performance of causal discovery in the setting, it can be helpful to explain the causal representations. If there is any in the appendix, it would be good to include part of the analysis in the main content. Moreover, it would be more convincing to include a 
clarity,_quality,_novelty_and_reproducibility: (-) The paper is a bit difficult to read and it is not clear in the beginning what and why are causal variables and causal representation. 

(-) The problem is important; whereas, the proposed solution relies on access to the intervention of each causal variable. It becomes less significant and interesting with the proposal. (I do reckon the effort on the proof.) Maybe try to justify how reasonable the setting is.

(-) It is not clear to me how the causal variables are determined. How the number of latent variables is determined and how the invertible function is constructed when the dimensions of the observation and the latent spaces are different?

summary_of_the_review: My main concern is the assumption of access to the interventions and the significance of the contributions can be limited. Moreover, it would be more convincing with real-world data experiments.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper proposes a causal representation learning method, iCITRIS, to solve the instantaneous effects of time series data via latent intervention. Specifically, iCITRIS learn a temporal process using VAE, where the latent variables can be identified by the construction. Then the structure learning methods such as NoTears and Enco are employed to learn the relations of latent variables.
Beyond that, to solve the instantaneous effects, it assumes that the intervention targets are observed. Overall, as an extension of CITRIS, this method shows the ability to allow instantaneous effect in both theoretical and experimental perspectives.

strength_and_weaknesses: Strength:
1) The task to identify the latent variables are very important to understand the temporal dynamic process.
2) The problem of the instantaneous effect is challenging and ubiquitous, which has the potential for improving time-series representation.
3) Both theoretical and experimental evidence shows that the proposed method is effective to solve the instantaneous effect problem.

Weaknesses:
1) This method assumes that the intervention targets are observed.  However, these interventions act on the latent variables.  How can we intervene the latent variables? Is it possible for the real-world application to observe the intervention targets?
2) All experiments are conducted on the simulation environment, such as Temporal Causal3DIdent and Pinball. These datasets are far away from real-world videos where the latent variables are unseen and uncontrollable.  Could this method is used for real-world video benchmarks, such as traffic event (Sutd-trafficqa[1]) or human actions (Oops[2]) . These datasets are more important to evaluate causal representation learning.
3) The contribution of this paper should be further highlighted. It is suggested to separate the original CITRIS method and this extension to avoid overlap and confusing contribution, e.g. adding a baseline method section to introduce CITRIS.

[1]Xu L, Huang H, Liu J. Sutd-trafficqa: A question answering benchmark and an efficient network for video reasoning over traffic events[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 9878-9888.

[2]Epstein D, Chen B, Vondrick C. Oops! predicting unintentional action in video[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 919-929.
 
clarity,_quality,_novelty_and_reproducibility: Clarity:  This paper is almost clear and well-written.  It is better to add a separate section to introduce CITRIS to help understand the difference and contributions.
Novelty: The problem and method are novel.  Just concerned with the rationality of the assumptions.
Reproducibility: code is attached.

summary_of_the_review: Overall, I almost like this paper and think it focuses on an important challenge. However, the rationality of the assumptions that the intervention of latent variables can be accessed seems an issue.
Balancing the positive and negative points, I think this paper should be further polished. More experiments using real-world data are needed.


Most concerns are addressed in the responses. I would like to raise my score accordingly.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The authors deeply dived into CAUSAL REPRESENTATION LEARNING on temporal sequences of observations and argue that temporal sequences of observations may still contain instantaneous causal relations in practice. 
In order to model the causal structure of the temporal sequences of observations with  INSTANTANEOUS AND TEMPORAL EFFECTS, the authors propose a new method iCITRIS based on CITRIS.
strength_and_weaknesses: Strength of the paper:
1. The paper investigates a new problem setting in which the causal variables may contain instantaneous causal relations. 
2. The paper offers a detailed explanation of the motivation and method for modeling the instantaneous effects in causal variables.
3. Sufficient experiments are conducted to evaluate the effectiveness of the proposed model in modeling the instantaneous effects of causal variables.
4. A detailed SUPPLEMENTARY MATERIAL is attached after the main paper, in which some proofs for assumptions and theories are also contained.


Weakness of the paper:

There is little description of related models, such as CITRIS, however, that is not a big issue.
clarity,_quality,_novelty_and_reproducibility: From the definition of the problem, and discussion to the proposed model, the paper is organized in a clear style. 

The paper investigates a new problem setting, which is a novelty. Following the assumptions, the authors offer a solid solution for that problem.

The proposed model is based on CITRIS, whose code has been released on GitHub. So it is not difficult to reproduce the experimental results reported in the paper.
summary_of_the_review: In the paper, the authors investigate a new problem setting, which is a novelty.
Meanwhile, a detailed discussion is stated and an effective model for causal variables identification is proposed.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper describes iCITRIS, a generalization of CITRIS [Lippe et al 2022] to allow for “instantaneous effects”: i.e. they allow dependencies between the latent variables within a given time step, as opposed to enforcing conditional independence of the latents at time t given the latents at time t-1. They start by showing that it is impossible to identify latents using only the CITRIS assumptions (known intervention targets & time series observations), because we can’t distinguish between independent latents $P(z_1, z_2) = P(z_1)P(z_2)$ and entangled non-independent latents that have the same joint. However, if you have access to “partially perfect” interventions that break the instantaneous dependencies, then you can recover the same identifiability results as those provided in CITRIS. They support their theory with a series of nice empirical demonstrations that show strong disentanglement on data generating processes with slightly weaker assumptions than their theory.
strength_and_weaknesses: Strengths
 - Instantaneous effects are an important problem to address if we are going to make causal representation learning practical, so I like the clear articulation of the setting and the problems that arise. The light / light switch example is a really nice simple illustrative example, but you could probably strengthen this motivation by finding more settings where this arrises: e.g. it is common in natural science applications where you can only measure a system infrequently (very common in biology); you want to argue that this is very common in practice (I think this is likely), and not just a feature of things that occur at the speed of light.
 - There’s a really nice discussion of the assumptions in the appendix. Space is always an issue, but as much as possible of this should be moved into the text, because it does a better job than the main text of articulating what we’re ruling out with the assumptions. 

Weaknesses:
 - The assumption that we know the intervention target is still very strong. This was true of CITRIS and is true here too. I don’t think it’s grounds for rejection, but I do think it severely limits what we can apply this method to.
 - Assumption 5’s symmetry breaking condition seems tricky to verify. The appendix articulates a nice example of a failure case, and gives sufficient conditions for gaussian (interventions need to modify means) - do you have sufficient conditions for any other distributions? You say, “this assumption will likely hold in most practical scenarios” - is that because most practical scenario involve Gaussians? Or some other reason?
 - The assumption that there is a bijection between X and both C and E is strong given the critique of methods that rely on counterfactual information (Locatello et al 2020, Brehmer et al 2022 and Ahjua et al 2022). It implies that the challenges of counterfactuals are reduced to “just” solving disentanglement (of course this isn’t an easy task), because we succeed in disentangling, there is no uncertainty about the noise variables in the abduction step, so counterfactuals are known exactly. I don’t mind this assumption in this context — it’s just saying that we see all the latents rendered visually — I’m having had time deciding whether it really is weaker than the perturbation assumptions. 
 - A lot of the detail about the actual implementation of the approach is hidden in a very long appendix - I know it’s just maximum likelihood wrt to the appropriate interventional distributions, but we need a little more detail in the main text to know how that translates to a loss function (e.g. are the distriubiutions over latents conditionally gaussian? Something else? ) and how the various components of the method are parameterized.
 - I think a running example would really help. There are a lot of moving pieces, so if you could have an example to keep referencing, it’d keep track of all the variables and relationships. E.g. C = [light state, switch state, …], X = pixel level image of the light scene, G = … and so on. If you just look at how many variables show up in Definition 4.3, you can see that it’s hard for a reader to track all the pieces. 
 - Minimal causal variables were not well explained. I know this is a concept from Lippe et al 2022, but I would have liked to see an explanation here too, in order to make this paper self-contained.

clarity,_quality,_novelty_and_reproducibility:  - This is high quality original work.
 - I would have a really hard time reproducing this from the text alone. That may be because the method is just complex, but I think that the author could probably do a better job of illustrating the various piece that need to be implemented - a good figure would go a long way. They do supply code though.
 - I thought the Preliminaries (Appendix C1) section in the appendix was actually a really nice summary of the method - I found only really understood what you were doing once I read that. I know space is constrained, but augmenting figure 1 to include as much as possible of that information would be helpful to help keep track of all the moving pieces in this.
summary_of_the_review: This is a nice paper that deals with an important problem and has strong experimental results. My main issues are with presentation - there are a lot of elements and the presentation could help the reader keep track of them all better.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: This paper studies the problem of causal representation learning from high-dimensional observations in the presence of instantaneous effects. Overall, reviewers considered the work interesting and novel. Therefore, I recommend the acceptance of the paper. 
justification_for_why_not_higher_score: The paper is interesting and worth publishing, but it's not very novel, insightful, or surprising.
Decision: Accept: poster