Paper:
Understanding causes and effects in mechanical systems is an essential component of reasoning in the physical world. This work poses a new problem of counterfactual learning of object mechanics from visual input. We develop the COPHY benchmark to assess the capacity of the state-of-the-art models for causal physical reasoning in a synthetic 3D environment and propose a model for learning the physical dynamics in a counterfactual setting. Having observed a mechanical experiment that involves, for example, a falling tower of blocks, a set of bouncing balls or colliding objects, we learn to predict how its outcome is affected by an arbitrary intervention on its initial conditions, such as displacing one of the objects in the scene. The alternative future is predicted given the altered past and a latent representation of the confounders learned by the model in an end-to-end fashion with no supervision of confounders. We compare against feedforward video prediction baselines and show how observing alternative experiences allows the network to capture latent physical properties of the environment, which results in significantly more accurate predictions at the level of super human performance.

Review:
Summary of what the paper claims and contributes
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This paper presents a new object forecasting task in the setting of "intuitive physics" that requires counterfactual reasoning and also presents method to perform this task. The task is to predict the alternative future trajectory of objects in 3D simulation given a video (A_frame,B_frames) of how the objects move in one situation and an alternative single frame (C_frame) that corresponds to modifying object position(s) at the first timestep of the input video. Due to unobserved confounding factors such as mass and friction that are not directly observable in either the video or the intervened frame, successful forecasting requires either implicitly or explicitly estimating these confounders. The motivation for this task is the hypothesis that counterfactual reasoning is a necessary component to forecast in general, unobserved situations. Prior work on explicit causal reasoning mainly concerns lower-dimensional problems, and prior work on high-dimensional "intuitive physics" has not evaluated the capability of models to perform counterfactual reasoning.
The paper proposes a neural network that learns to implicitly predict sufficient statistics of confounders of the situation. Training occurs in two stages. First, it is trained to estimate 3d positions of objects from images given pairs of (image, ground truth 3d positions). Then, it is trained to forecast future unobserved object positions with a graph convolutional network. Inference is performed by estimating the current object positions in all of the frames of the input (A_frame, B_frames), and then using the final representation of the input situation as the representation of confounders that is fed into the alternative-future prediction.
The experiments find:
1) Humans are not very good at these specific tasks, as evidenced by a simple position-copying baseline that outperforms humans. Given that the simple copying baseline is so performant, the claim in the abstract of "super human performance"  is also true for the copying baseline, and therefore that claim isn't very meaningful and should be removed.
2) The proposed approach outperforms position-copying and two non-counterfactual approaches that do not reason (explicitly or implicitly) about confounders,.
3) The approach attempts to show that confounder representations are learned as the main evidence that the model can perform counterfactual reasoning. However, several aspects of this experiment are unclear, making the claim difficult to evaluate.
Finally, although the abstract and the main text say that the task is to perform counterfactual forecasting in high-dimensional scenarios in a unsupervised way, the paper ends up using direct supervision of the object positions. With this knowledge, the problem is not that different than counterfactual forecasting in low-dimensional settings, as the positions of the objects at all frames corresponding to \mathbf{A}, \mathbf{B}, and \mathbf{C} could simply be extracted with this detector. Thus, through this supervision the approach seems to circumvent most of the challenges imposed by high-dimensional forecasting.
Evaluation
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>Originality:
Are the tasks or methods new?
The task of counterfactual forecasting from high-dimensional observations is new. The proposed method extends prior work to the new task.
Is the work a novel combination of well-known techniques?
Yes.
Is it clear how this work differs from previous contributions?
Yes.
Is related work adequately cited?
To my knowledge, yes.
>Quality:
Is the submission technically sound?
Yes.
Are claims well supported by theoretical analysis or experimental results?
Some of the claims are supported, but the evidence for the counterfactual representation recovery claim is currently unclear. Additionally, the high-level motivation of counterfactual forecasting in high-dimensional settings is significantly undermined by the use of low-level supervision information that could feasibly be predicted at test time.
Is this a complete piece of work or work in progress?
It seems relatively complete.
Are the authors careful and honest about evaluating both the strengths and weaknesses of their work?
Weaknesses are not explicitly explored. It would be good to include discussion that illuminates or hypothesizes when and why the method fails, and how it could be made to perform better.
>Clarity:
Is the submission clearly written?
The submission is mostly clear, however there are some significant ambiguities in the experiments.
Is it well organized?
Yes.
Does it adequately inform the reader?
Mostly -- see ambiguities.
>Significance:
Are the results important?
It's currently unclear whether the results are significant, but it's clear the task is important (only when not supervised by object positions). However, justification with respect to other recent intuitive physics simulators is needed.
Are others (researchers or practitioners) likely to use the ideas or build on them?
Other researchers are likely to build on the task and data released.
Does the submission address a difficult task in a better way than previous work?
N/A (new task)
Does it advance the state of the art in a demonstrable way?
There is no SOTA on this new task.
Does it provide unique data, unique conclusions about existing data, or a unique theoretical or experimental approach?
Yes, one of the main strengths of the paper is the task and dataset that will be released. This experimental approach could be very useful for future research.
Additional feedback
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The experiments to validate the cofounder estimation is unclear. Ambiguities:
0) In the text, the classification of mass and friction is presented as follows: "The obtained results are shown in Table 6 (middle)", yet the results are in Table 5. The impact of confounder supervision is described as follows "we do, however, explore what effect supervision could have on performance, as shown in Table 5", yet the results are in Table 6. These typos, when combined with the layout of Tables 5 and 6, makes it very difficult to interpret these results.
1) What is feedforward method? I can't find any description. Is this the IN or the NPE baseline, or something different? Why aren't both used? Without more details, its unclear that the proposed method actually learns confounder representations better than other methods.
2) What is the "random baseline level"? Is this just chance results from a uniform distribution as a baseline classifier?
3) What are the details of the classifier that predicts the mass and friction coefficients?
It is a bit odd to include comparisons to humans on a task on which the humans are outperformed by a very simple baseline. Either downplay the role of these comparisons, or remove these comparisons, or change the task such that humans cannot be outperformed by a simple baseline.
More details about the NPE and IN baselines are missing. Specifically, do they also leverage access to the ground-truth positions at training time? If so, how do they do this? This discussion is important for the paper to be self-contained. 
Page 9 "The CophyNet model also consistently outperforms the augmented variants..." the outperformance isn't present in all cases (i.e. not in 4->2 or 4->3); change to "usually".
It's not clear if there is any stochasticity in the dynamics of the created data (which would be relevant to modeling and evaluation).
It wasn't clear until page 7 that the training leveraged GT positions in 3d space. The training objective (equation after Eq (5)) has an undefined o^*, which the reader must infer corresponds to GT, or read appendix A.1, to understand. The description of the method was introduced in terms of A, B, C, and D which are images or videos. To me, the fact that GT 3D positions are used seems like it was "buried" later in the paper, rather than described up-front alongside the introduction of A, B, C, and D.
The meaning of the colors, if any, in Fig 2 is unclear. If they are meant to illustrate observed information, perhaps grayscale shading could be used instead, as is standard in PGMs.
The latent representation in Fig 4 appear to emerge just from the red object's RNN, rather than all of the objects RNNs.
Tables 1 and 2 appear very far from their description in the text.
More discussion is needed that relates the tasks in to the tasks in Riochet2018 and Bakhtin2019. It's not clear why a new benchmark is needed without this discussion. Specifically, could the proposed method and evaluation be applied to these benchmark simulators? If not, why not? The answers to these questions seem to be the main motivation for the proposed tasks.
The simplest baseline would be to simply stack (A,B,C) as contextual input to a learned regressor of the future positions in D. Another simple baseline is to use the learned position estimator to predict all of the positions in (A,B,C) and use these positions as input to a learned regressor.
Use two backtick characters instead of the double-quote character for starting forward quotations.
Recommendation
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Taken together, the novelty of the task and dataset, the discrepancy between problem motivation (high-dimensional counterfactual reasoning vs. uses low-dimensional ground-truth), and the partial clarity of the experimental results, leads me to conclude that although the proposed task and data are quite interesting and promising, the paper needs significant work to clarify its motivation and justification of its claims. I would rate the paper borderline, however it appears I'm forced to discretize to WA or WR. I choose WA because I am optimistic that the authors could address my doubts.