Causal Model

Causal modeling aims to uncover cause-and-effect relationships within data, going beyond simple correlations to understand how variables influence each other. Current research focuses on developing algorithms and model architectures, such as Bayesian networks and linear structural equation models, that can effectively learn causal structures from observational and interventional data, even in the presence of hidden variables or confounding factors. This work is crucial for improving the reliability and interpretability of machine learning models across diverse fields, from robotics and healthcare to economics and climate science, by enabling more accurate predictions and informed decision-making under uncertainty. Furthermore, research is actively addressing challenges in model evaluation and the development of robust methods applicable to high-dimensional and complex data.