Credibility Methods in Causal Inference

Abstract

How to credibly investigate causal relationships is a central issue in contemporary econometrics. This paper systematically reviews the logical chain of modern causal inference: from the paradigm shift of the credibility revolution, to the counterfactual framework for defining causality, to the assumptions and limitations of OLS regression, then to the concept of identification as the bridge connecting data to truth, and finally to the art of research design. At each stage, a study on the “Citation Trap” serves as a running case example to concretize abstract methodological principles. The analysis shows that a credible causal inference study begins with a clear “what if” question, approximates the ignorability assumption through control variables and fixed effects, and constructs a complete chain of evidence by ruling out alternative explanations and testing mechanism implications. The conclusion is that the essence of econometrics lies not in complex models or techniques, but in rigorous research design that makes assumptions plausible and conclusions robust to scrutiny.