A way of studying relationships between economic variables by starting with a theory, choosing a mathematical model that represents that theory, and then checking if the real-world data supports it.

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This approach is classical econometrics. It centers on taking an economic theory, turning it into a mathematical model that expresses relationships between variables, and then using real data to estimate the model and test whether the data support the theory. The process often involves specifying a functional form (like a linear regression), choosing appropriate estimation methods (such as ordinary least squares under the usual assumptions), and conducting hypothesis tests and diagnostic checks to see if the theoretical relationship holds in practice. This emphasis on theory-driven model specification followed by empirical validation differentiates classical econometrics from other fields.

As for the other terms: inference is a broad statistical concept about drawing conclusions from data, but it doesn’t by itself describe the theory-to-model-to-data testing cycle central to econometrics. Machine learning focuses on predictive accuracy and flexible models, often with less emphasis on grounding the model in economic theory. Data mining aims to discover patterns in large datasets without necessarily tying findings to an underlying theory.