Unraveling Causality in Imitation Learning

This chapter explores the intricacies of causality in imitation learning, emphasizing the distinction between causation and correlation. It discusses the implications of these concepts in systems like autonomous vehicles and shares a novel approach to identifying causal relationships amid spurious correlations. The chapter also highlights the limitations of imitation learning compared to model-based reinforcement learning, showcasing key findings on the challenges and advancements in this area.