Causal Mediation Analysis

Causal mediation analysis aims to dissect the causal pathways through which an intervention influences an outcome, identifying both direct and indirect effects via mediating variables. Current research emphasizes developing robust and efficient estimation methods, particularly for high-dimensional or indirectly observed mediators, often leveraging machine learning techniques like deep neural networks and reinforcement learning within various model architectures. This analytical framework is increasingly vital across diverse fields, from understanding media effects and bias in AI models to evaluating the impact of healthcare interventions and interpreting the inner workings of complex systems like large language models.