Causal Discovery in AI Faces Challenges of Selection Bias
Analysis
This ArXiv paper explores a critical challenge in causal discovery, namely, selection bias. Addressing this issue is essential for developing robust and reliable AI systems that can accurately infer causal relationships from data.
Key Takeaways
- •Addresses the problem of selection bias in causal discovery.
- •Focuses on latent variable causal discovery.
- •Aims to improve the reliability of AI systems that infer causal relationships.
Reference
“The paper focuses on latent variable causal discovery.”