Causal Analysis

Causal analysis aims to uncover cause-and-effect relationships within complex systems, moving beyond mere correlations to understand underlying mechanisms. Current research emphasizes developing robust methods for causal discovery and inference from observational data, often employing machine learning techniques like Bayesian networks, double machine learning, and structural causal models, as well as adapting these methods for high-dimensional data and time series. This field is crucial for advancing scientific understanding across diverse disciplines and informing decision-making in areas such as healthcare, economics, and AI development, by enabling more reliable predictions and interventions.