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 read- AI
- Causality
- Trustworthy AI
Finding new uses for drugs with generative AI
ResearchMichal Rosen-Zvi4 minute read- Accelerated Discovery
- AI
- Causality
- Generative AI
- Healthcare
Publications
- Zirui Yan
- Dennis Wei
- et al.
- 2024
- AISTATS 2024
- Dongxia Wu
- Ide-San Ide
- et al.
- 2024
- AISTATS 2024
- Zijun Cui
- Hanjing Wang
- et al.
- 2024
- AISTATS 2024
- Naiyu Yin
- Tian Gao
- et al.
- 2024
- AAAI 2024
- Jiaqi Zhang
- Kristjan Greenewald
- et al.
- 2023
- NeurIPS 2023
- 2023
- NeurIPS 2023