HIDDEN DRIVERS IN CAUSAL INFERENCE: ADVANCED TECHNIQUES FOR HANDLING CONFOUNDERS

Abstract

This comprehensive article explores the evolution and impact of causal inference techniques across various industrial and technological domains. The article examines how causal inference has transformed traditional statistical approaches, particularly in addressing confounding variables and hidden drivers in complex systems. The article highlights significant advancements in methodological frameworks, including backdoor adjustment, instrumental variables, and sensitivity analysis, demonstrating their practical applications in energy systems, industrial control, and healthcare. The article further explores the integration of causal inference with quantum computing and machine learning, revealing substantial improvements in system performance, operational efficiency, and decision-making accuracy. The article also examines cross-domain applications, particularly in healthcare and environmental monitoring, showcasing the versatile nature of causal inference frameworks in addressing complex analytical challenges.