Causal modeling is crucial to the effectiveness and trust of AI by ensuring that actions lead to intended outcomes. We study the inference of causal effects and relationships, as well as the application of causal thinking to out-of-distribution generalization, fairness, robustness, and explainability.
Tools + code
Qing Wang, Larisa Shwartz, et al.2021CLOUD 2021
Futoshi Iwama, Miki Enoki, et al.2021SMDS 2021
Praveen Venkateswaran, Vinod Muthusamy, et al.2021KDD 2021
Rui Chen, Sanjeeb Dash, et al.2021ICML 2021
Yue Yu, Tian Gao, et al.2021ICML 2021
Abhin Shah, Kartik Ahuja, et al.2021ICASSP 2021