Exploring the Role of Causal Inference Methods in Establishing Statistical Relationships in Observational Studies

Posted: Jul 15, 2014

• Sophia Walker
• Jacob Garcia
• Olivia Thompson

Abstract

Observational studies represent a cornerstone of empirical research across numerous disciplines, from epidemiology and economics to education and social sciences. These studies enable researchers to investigate relationships between variables in settings where randomized controlled trials are impractical, unethical, or impossible to implement. However, the fundamental challenge in observational research has always been the difficulty in distinguishing genuine causal relationships from spurious correlations driven by confounding factors. Traditional statistical methods, particularly regression analysis, have been widely employed to address this challenge, but they often fall short in establishing credible causal claims due to their inherent limitations in handling unmeasured confounding and selection bias. The landscape of causal inference has evolved dramatically in recent decades, with the development of sophisticated methodologies specifically designed to address the limitations of conventional approaches. These methods, grounded in the potential outcomes framework and directed acyclic graphs, provide formal