Common Cause

The "common cause" principle investigates how seemingly correlated events might share an underlying, often unobserved, causal factor. Current research focuses on identifying and characterizing these latent common causes, particularly in complex systems like neural networks and probabilistic models, employing techniques such as Bayesian nonparametric inference and maximum likelihood estimation to disentangle confounding influences. Understanding common causes is crucial for accurate causal inference, improving the robustness of AI systems, and resolving paradoxical relationships in observational data across diverse fields. This work has implications for areas ranging from autonomous systems safety to resolving inconsistencies in epidemiological studies.