Causal discovery is a methodology for learning causal graphs from data, and LiNGAM is a
well-known model for causal discovery. This paper describes an open-source Python …

Recently, structure learning of directed acyclic graphs (DAGs) has been formulated as a
continuous optimization problem by leveraging an algebraic characterization of acyclicity …

This monograph discusses statistical causal discovery methods for inferring causal
relationships from data derived primarily from non-randomized experiments. Specifically, I …

Wind energy is increasingly vital globally, requiring precise output forecasting for stable,
efficient power systems. However, this becomes particularly challenging for newly built wind …

Predicting the presence of major depressive disorder (MDD) using behavioural and
cognitive signals is a highly non-trivial task. The heterogeneous clinical profile of MDD …

Causal discovery finds widespread applications, ranging from estimating treatment
effectiveness in medicine, analyzing policy impacts in economics, to constructing predictive …

A central problem of science is to elucidate the causal mechanisms underlying natural
phenomena and human behavior. Statistical causal inference offers various tools to study …

Causal relationships play an irreplaceable role in revealing the mechanisms of phenomena
and guiding intervention actions. However, due to limitations in existing frameworks …

Digital health technologies such as wearable devices have transformed health data
analytics, providing continuous, high-resolution functional data on various health metrics …

Causal discovery in the presence of unobserved common causes from observational data
only is a crucial but challenging problem. We categorize all possible causal relationships …