Causality Seminar

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

2023-06-09A Uniformly Consistent Estimator of non-Gaussian Causal Effects Under the k-Triangle-Faithfulness Assumption.   Speaker: Shuyan Wang 

2023-05-12Probabilities 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 

2023-04-21MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models.   Speaker: Erdun Gao

2023-04-07Transformed Independent Noise (TIN) Condition in LiNGAM and its Application in Causal Discovery with Latent Variables.   Speaker: Haoyue Dai

2023-03-24Causal Learning in the Presence of Latent Confounders.   Speaker: Tianzuo Wang

2023-03-10Causal discovery for linear mixed data.   Speaker: Yan Zeng

2023-02-24Causal Machine Learning.   Speaker: Chaochao Lu

2023-02-10:  The Causal-Neural Connection: Expressiveness, Learnability, and Inference.   Speaker: Kevin Xia

2023-02-03On the Identifiability of Nonlinear ICA: Sparsity and Beyond.   Speaker: Yujia Zheng

2023-01-20Experimental 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