Correlation Does Not Imply Causation: An Econometric Perspective

Abstract

Purpose – The main aim of this paper is to clarify the distinctions between statistical correlation and causation, addressing the research question: How can researchers avoid misinterpreting correlations as causal relationships in empirical analysis?

Methods/Design-This theoretical paper reviews concepts from economics and econometrics, discussing pitfalls like spurious correlations, Simpson’s paradox, and omitted variable bias. It examines causal identification methods, including randomized controlled trials (RCTs), quasi-experiments, instrumental variables (IV), and difference-in-differences (DiD), illustrated through examples from education, labor economics, healthcare, and macroeconomics.

Findings-Key pitfalls include spurious associations and biases that obscure true causality. Methods like RCTs and IV effectively isolate causal effects, as demonstrated in accessible case studies, revealing that correlations alone fail to establish cause-and-effects.

Conclusion/Implications-Rigorous causal inference, guided by theory and robust design, is vital for credible analysis. Implications include improved policy-making, business decisions, and academic rigor, urging greater emphasis on causal methods to prevent erroneous conclusions.

Limitations of the Study-As a conceptual review, it lacks original empirical data and may not cover all domain-specific nuances.

Originality of the Study-This work uniquely integrates diverse econometric tools with real-world examples across fields, distinguishing it from prior discussions by emphasizing practical application for non-specialists.