Practical Challenges in Conditional Independence Testing

This ArXiv paper likely explores the computational and statistical complexities of conditional independence testing, a crucial aspect of causal inference and machine learning. Understanding these practical limitations is vital for developing robust and reliable AI models, and the paper likely contributes to that understanding.

• •The paper focuses on the real-world difficulties of applying conditional independence tests.
• •This research likely offers insights into the efficiency and accuracy of different testing methods.
• •The findings likely have implications for model selection and causal discovery algorithms.