Causality
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.
Our work
Machine learning: From “best guess” to best data-based decisions
ReleaseYishai Shimoni and Ehud Karavani7 minute readFinding new uses for drugs with generative AI
ResearchMichal Rosen-Zvi4 minute read
Publications
- Naiyu Yin
- Hanjing Wang
- et al.
- 2024
- ECCV 2024
- 2024
- BPM 2024
- Victor Akinwande
- Megan Macgregor
- et al.
- 2024
- IJCAI 2024
- Christopher Lohse
- Jonas Wahl
- 2024
- UAI 2024
- Grace Guo
- Lifu Deng
- et al.
- 2024
- FAccT 2024
- Zirui Yan
- Dennis Wei
- et al.
- 2024
- AISTATS 2024