Granger Causal Structure Reconstruction from Heterogeneous Multivariate Time Series
Granger causal structure reconstruction is an emerging topic that can uncover causal relationship behind multivariate time series data. In many real-world systems, it is common to encounter a large amount of multivariate time series data collected from heterogeneous individuals with sharing commonalities, however there are ongoing concerns regarding its applicability in such large scale complex scenarios, presenting both challenges and opportunities for Granger causal reconstruction. To bridge this gap, we propose a Granger cAusal StructurE Reconstruction (GASER) framework for inductive Granger causality learning and common causal structure detection on heterogeneous multivariate time series. In particular, we address the problem through a novel attention mechanism, called prototypical Granger causal attention. Extensive experiments, as well as an online A/B test on an E-commercial advertising platform, demonstrate the superior performances of GASER.
The paper proposes a novel way of reconstructing Granger causal structures using a differentiable neural network architecture that contains attention modules that are proportional to the Granger causality of the input layers. Furthermore, the architecture blends individual-specific induced causal structures and cross-population prototypical causal structures. The paper has an extensive experimental section on which the proposed method shows impressive improvements in causal discovery performance and predictive performance on par with state-of-the-art. As main contributions the paper: * proposes a novel architecture * shows its values using extensive experiments Overall, I find the paper well-written with a clear description of the proposed architecture and clear experiments showing the importance of the architectural choices. A possible downside is the relative lack of novelty, since the method seems like a reasonable extension of the existing work. However, I think this counterbalanced by the excellent results on the causal discovery task and the extensive nature of the experiments. In terms of suggestions, I think an illustrative example of the granger attention on an artificial / toy example would help a lot to give an intuitive understanding on how the method works and how the causal structure is being built.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Granger Causal Structure Reconstruction from Heterogeneous Multivariate Time Series
Granger causal structure reconstruction is an emerging topic that can uncover causal relationship behind multivariate time series data. In many real-world systems, it is common to encounter a large amount of multivariate time series data collected from heterogeneous individuals with sharing commonalities, however there are ongoing concerns regarding its applicability in such large scale complex scenarios, presenting both challenges and opportunities for Granger causal reconstruction. To bridge this gap, we propose a Granger cAusal StructurE Reconstruction (GASER) framework for inductive Granger causality learning and common causal structure detection on heterogeneous multivariate time series. In particular, we address the problem through a novel attention mechanism, called prototypical Granger causal attention. Extensive experiments, as well as an online A/B test on an E-commercial advertising platform, demonstrate the superior performances of GASER.
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>



The paper proposes a novel way of reconstructing Granger causal structures using a differentiable neural network architecture that contains attention modules that are proportional to the Granger causality of the input layers. Furthermore, the architecture blends individual-specific induced causal structures and cross-population prototypical causal structures. The paper has an extensive experimental section on which the proposed method shows impressive improvements in causal discovery performance and predictive performance on par with state-of-the-art. As main contributions the paper: * proposes a novel architecture * shows its values using extensive experiments Overall, I find the paper well-written with a clear description of the proposed architecture and clear experiments showing the importance of the architectural choices. A possible downside is the relative lack of novelty, since the method seems like a reasonable extension of the existing work. However, I think this counterbalanced by the excellent results on the causal discovery task and the extensive nature of the experiments. In terms of suggestions, I think an illustrative example of the granger attention on an artificial / toy example would help a lot to give an intuitive understanding on how the method works and how the causal structure is being built.
assistant
The paper proposes a novel way of reconstructing Granger causal structures using a differentiable neural network architecture that contains attention modules that are proportional to the Granger causality of the input layers. Furthermore, the architecture blends individual-specific induced causal structures and cross-population prototypical causal structures. The paper has an extensive experimental section on which the proposed method shows impressive improvements in causal discovery performance and predictive performance on par with state-of-the-art. As main contributions the paper: * proposes a novel architecture * shows its values using extensive experiments Overall, I find the paper well-written with a clear description of the proposed architecture and clear experiments showing the importance of the architectural choices. A possible downside is the relative lack of novelty, since the method seems like a reasonable extension of the existing work. However, I think this counterbalanced by the excellent results on the causal discovery task and the extensive nature of the experiments. In terms of suggestions, I think an illustrative example of the granger attention on an artificial / toy example would help a lot to give an intuitive understanding on how the method works and how the causal structure is being built.