causalstructuren
Causalstructuren refers to the systematic arrangement of cause-and-effect relationships between variables or components in a system, used to explain how outcomes arise from mechanisms and interventions.
They are often represented as causal graphs or structural equations. Directed acyclic graphs (DAGs) are common
Key concepts include causal mechanisms, interventions (the do-operator), and counterfactuals. Identifiability concerns whether causal effects can
Applications span epidemiology, economics, social sciences, AI safety, and policy evaluation. In machine learning, causal structures
Constructing causal structures relies on domain knowledge and data. Common assumptions include causal sufficiency and faithfulness.
The concept has roots in philosophy and statistics, with modern formalism developed in the late 20th and