reviews: 
Comments: The paper delves into the spatio-temporal challenges inherent in earth sciences, encompassing areas like meteorological forecasting and urban computing. Given the hurdles of data sparsity and imbalance, the authors put forth a novel causal prediction framework dubbed "NuwaDynamics". Operating through two phases - "Discovery" and "Update" - this paradigm pinpoints causal regions within datasets and endows the model with causal reasoning prowess. The authors also substantiate their methodology with proofs drawn from causal theory and employ a myriad of experimental results to demonstrate its efficacy across various spatio-temporal benchmark tests, especially in tasks like extreme weather forecasting and long-term temporal predictions. In a nod to fostering collaboration and transparency, the code from this research has been shared with the public, marking a valuable contribution to the open-source arena.
Strengths: 1. The paper is well-written and exhibits a commendable layout; the motivation is intuitive and clear.

2. It is interesting to employ ViT and Mixup in spatiotemporal data for causal environmental discovery and augmentation, and the work can potentially expand the model's latent observational space in many scarce scenarios.

3. Extensive experiments demonstrate the effectiveness of the framework. The authors introduced architectures tailored for both ViT and non-ViT, while also including control experiments across frameworks such as RNN and CNN. Comparisons across multiple mainstream tasks, including transfer learning and super-resolution prediction, further demonstrates its capabilities in tasks like extreme event prediction.

4. Detailed theoretical validation based on causal manipulation is provided to demonstrate the feasibility of the framework. Interestingly, in real-world scenarios, the author doesn't explore all possible environments but instead introduces partial perturbations, which conveniently addresses the computational constraints and limited data availability in practice.
Weaknesses: 1. I am concerned about whether there are clear guiding principles for environmental augmentation. Especially when it comes to the mixup technique, is it possible to automatically adjust the fusion hyperparameters of each environmental patch based on a specific dataset? If this cannot be achieved at the moment, I hope the authors will offer potential solutions in their section of future work .

2. I also have a keen interest in environmental perturbation methods. In fact, environmental perturbation can be considered a form of data augmentation. I wonder, are there any generative methods in computer vision (CV) that can be integrated into this? If the answer is affirmative, I hope the authors delve deeper into this in the "Related Work" and "Model" sections.

3. The experimental section of the paper is rich in content, and I am particularly interested in spatio-temporal system modeling. Current research literature mainly focuses on datasets with lower resolution, for example, roughly 256x256 in size. I hope the authors can consider more experiments to explore whether these methods remain competitive on high-resolution datasets. If the authors can achieve superior results on higher-resolution datasets, I might consider further raising my score.
Recommendation: 10: strong accept, should be highlighted at the conference
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 4 excellent
Clarity: 4 excellent