Inspired by Causality Discussion Group organized by Matej Zecevic, I co-organized the Causality Seminar with Dr. Yun Zhou. At this stage, the talks are given in Chinese. But we aim to have speakers, not limited to Chinese researchers. A similar major Causal Inference Tutorial in Chinese can be found here. The meetings occurs at 21:00 Beijing/8:00 EST. The Tencent Meeting ID is: 935-9229-8101. The record of talks is available here (Passcode: 1111). If you would like us to know about your work, please feel free to let us know, and we will be very happy to arrange a talk for you. Information of coming and past talks is listed as below:
2024-05-10: Relevance Inference based on Direct Contribution: Counterfactual Explanation to Deep Networks for Intelligent Decision-making. Speaker: Xinyu Li
2024-04-26: Testing Conditional Independence Between Latent Variables by Independence Residuals. Speaker: Zhengming Chen
2024-04-05: Counterfactual Explanation for Regression via Disentanglement in Latent Space. Speaker: Xuan Zhao
2024-03-08: Intervention generalization: A view from factor graph models. Speaker: Jialin Yu
2024-01-26: Invariant Learning via Probability of Sufficient and Necessary Causes. Speaker: Mengyue Yang
2024-01-12: Causal Discovery from Time Series Data. Speaker: Wei Song
2023-11-24: Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning. Speaker: Yuejiang Liu
2023-11-03: Causal Component Analysis. Speaker: Wendong Liang
2023-10-20: Causal Discovery and Causal-learn. Speaker: Wei Chen
2023-09-22: LOG: Active Model Adaptation for Label-Efficient OOD Generalization. Speaker: Jie-Jing Shao
2023-09-15: Invariant and transportable representations for anti-causal domain shifts. Speaker: Yibo Jiang
2023-07-07: Causal Reinforcement Learning. Speaker: Yan Zeng
2023-06-23: Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization. Speaker: Weijia Zhang
2023-06-16: Causal Inference with Non-IID Data under Model Uncertainty. Speaker: Chi Zhang
2023-06-09: A Uniformly Consistent Estimator of non-Gaussian Causal Effects Under the k-Triangle-Faithfulness Assumption. Speaker: Shuyan Wang
2023-05-12: Probabilities of Causation: Role of Observational Data. Speaker: Ang Li
2023-04-28: Causal inference with treatment measurement error: a nonparametric instrumental variable approach. Speaker: Yuchen Zhu
2023-04-21: MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. Speaker: Erdun Gao
2023-04-07: Transformed Independent Noise (TIN) Condition in LiNGAM and its Application in Causal Discovery with Latent Variables. Speaker: Haoyue Dai
2023-03-24: Causal Learning in the Presence of Latent Confounders. Speaker: Tianzuo Wang
2023-03-10: Causal discovery for linear mixed data. Speaker: Yan Zeng
2023-02-24: Causal Machine Learning. Speaker: Chaochao Lu
2023-02-10: The Causal-Neural Connection: Expressiveness, Learnability, and Inference. Speaker: Kevin Xia
2023-02-03: On the Identifiability of Nonlinear ICA: Sparsity and Beyond. Speaker: Yujia Zheng
2023-01-20: Experimental Design in Causal Models. Speaker: Jiaqi Zhang
2023-01-06: Continuous Optimization for Learning Bayesian Networks. Speaker: Tian Gao
2022-12-09: Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs. Speaker: Yongqiang Chen
2022-11-25: Rationale-based Data Augmentation for Explainable and Robust NLP. Speaker: Linyi Yang
2022-11-18: Optimal Transport for Causal Discovery. Speaker: Ruibo Tu
2022-11-11: Incorporating Structural Constraints into Continuous Optimization for Causal Discovery. Speaker: Zidong Wang
2022-10-28: Causal Discovery from Observational Data with Functional Causal Model. Speaker: Jie Qiao