DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images
Some fundamental limitations of deep learning have been exposed such as lacking generalizability and being vunerable to adversarial attack. Instead, researchers realize that causation is much more stable than association relationship in data. In this paper, we propose a new framework called do-calculus AutoEncoder(DO-AE) for deep representation learning that fully capture bivariate causal relationship in the images which allows us to intervene in images generation process. DO-AE consists of two key ingredients: causal relationship mining in images and intervention-enabling deep causal structured representation learning. The goal here is to learn deep representations that correspond to the concepts in the physical world as well as their causal structure. To verify the proposed method, we create a dataset named PHY2D, which contains abstract graphic description in accordance with the laws of physics. Our experiments demonstrate our method is able to correctly identify the bivariate causal relationship between concepts in images and the representation learned enables a do-calculus manipulation to images, which generates artificial images that might possibly break the physical law depending on where we intervene the causal system.
In summary: This paper is not ready for publication. The paper contains some potentially interesting ideas, but the presentation quality is not sufficient for publication. The paper should be substantially improved before re-submission. Strengths: - Causality is an important and established research area, and papers on this topic would be timely. - Paper contains some interesting ideas to integrate causality into an auto-encoder (but see weaknesses below) - Paper proposes a new dataset for evaluating causal mechanisms (but the approach is not evaluated) Weaknesses: - The quality of the writing is inappropriate for a scientific venue. Language throughout the paper is loose, eg *physics is a hot topic* or *People have studied causality for a long time* or *Causality is a bridge between science and philosophy* The paper should be re-written so that it is precise and clear. - The technical approach has several typos and lacks discussion of the approach. Instead, several high-level statements are made, with long equations. This makes appreciating the contribution of the paper difficult. - The dataset is potentially interesting, but it is artificial. A much more exciting dataset would be realistic data. - The experiments only evaluate the likelihood, but it is not clear whether this is on a training or testing set.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images
Some fundamental limitations of deep learning have been exposed such as lacking generalizability and being vunerable to adversarial attack. Instead, researchers realize that causation is much more stable than association relationship in data. In this paper, we propose a new framework called do-calculus AutoEncoder(DO-AE) for deep representation learning that fully capture bivariate causal relationship in the images which allows us to intervene in images generation process. DO-AE consists of two key ingredients: causal relationship mining in images and intervention-enabling deep causal structured representation learning. The goal here is to learn deep representations that correspond to the concepts in the physical world as well as their causal structure. To verify the proposed method, we create a dataset named PHY2D, which contains abstract graphic description in accordance with the laws of physics. Our experiments demonstrate our method is able to correctly identify the bivariate causal relationship between concepts in images and the representation learned enables a do-calculus manipulation to images, which generates artificial images that might possibly break the physical law depending on where we intervene the causal system.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



In summary: This paper is not ready for publication. The paper contains some potentially interesting ideas, but the presentation quality is not sufficient for publication. The paper should be substantially improved before re-submission. Strengths: - Causality is an important and established research area, and papers on this topic would be timely. - Paper contains some interesting ideas to integrate causality into an auto-encoder (but see weaknesses below) - Paper proposes a new dataset for evaluating causal mechanisms (but the approach is not evaluated) Weaknesses: - The quality of the writing is inappropriate for a scientific venue. Language throughout the paper is loose, eg *physics is a hot topic* or *People have studied causality for a long time* or *Causality is a bridge between science and philosophy* The paper should be re-written so that it is precise and clear. - The technical approach has several typos and lacks discussion of the approach. Instead, several high-level statements are made, with long equations. This makes appreciating the contribution of the paper difficult. - The dataset is potentially interesting, but it is artificial. A much more exciting dataset would be realistic data. - The experiments only evaluate the likelihood, but it is not clear whether this is on a training or testing set.
assistant
In summary: This paper is not ready for publication. The paper contains some potentially interesting ideas, but the presentation quality is not sufficient for publication. The paper should be substantially improved before re-submission. Strengths: - Causality is an important and established research area, and papers on this topic would be timely. - Paper contains some interesting ideas to integrate causality into an auto-encoder (but see weaknesses below) - Paper proposes a new dataset for evaluating causal mechanisms (but the approach is not evaluated) Weaknesses: - The quality of the writing is inappropriate for a scientific venue. Language throughout the paper is loose, eg *physics is a hot topic* or *People have studied causality for a long time* or *Causality is a bridge between science and philosophy* The paper should be re-written so that it is precise and clear. - The technical approach has several typos and lacks discussion of the approach. Instead, several high-level statements are made, with long equations. This makes appreciating the contribution of the paper difficult. - The dataset is potentially interesting, but it is artificial. A much more exciting dataset would be realistic data. - The experiments only evaluate the likelihood, but it is not clear whether this is on a training or testing set.