Causal Entropy

Causal entropy focuses on quantifying the causal influence of one variable on another by measuring changes in entropy following interventions. Current research emphasizes developing algorithms and models, such as maximum causal entropy methods and causal entropy boosting, to infer causal relationships from observational data, even in non-stationary or complex systems with hidden variables. This work is significant because it improves causal discovery and feature selection in machine learning, enabling more robust and interpretable models across diverse fields like reinforcement learning and dynamical systems analysis.