NSFC Key Program 2026 @ NBUTime is tight and the task is demanding. Let's all work together to accomplish this challenging mission! With full effort and unity, this is a vital opportunity for our team to shine. Step by step, with determination and cooperation, we can overcome any obstacle and achieve great success. Let's embrace this challenge with positive energy and mutual support-together, we will make it happen and create something truly remarkable! Solar Data
Causal InferenceIt is highly recommended to read the review article Causal Inference Meets Deep Learning: A Comprehensive Survey by Jiao et al. and the textbook Causal Inference & Machine Learning by Guo et al. . We also suggest watching the video An Introduction to Causal Inference in One Hour. Additionally, in line with the syllabus of the course Causality taught by Dr. Christina Heinze-Deml, the primary required textbooks and articles are those by Freedman et al \(^{1}\), Shalizi \(^{2}\), Maathuis et al \(^{3}\), Cinelli et al \(^{4}\), Peters et al \(^{5}\) and Shimizu \(^{6}\). A complete collection of these materials (including notes and slides) is available here. Supplemental information is provided in the Content Summary. Scientific ProblemWe strongly encourage all team members to read the insightful grant proposal by Prof. Xu. We also recommend the following papers for further reading: Causal Attention Deep-Learning Model for Solar Flare Forecasting by Zhang et al., and Causal Attention for Unbiased Visual Recognition by Wang et al..
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