= Econometrics
<Econometrics> uses <statistical models> to quantify economic relationships and to assess predictions or causal claims from economic data. The choice of variables and the mechanism generating observations matter as much as estimating the model coefficients.
For example, in a <linear regression> of log earnings on years of schooling, a slope $0.08$ corresponds to a fitted earnings ratio $e^{0.08}\approx1.083$ for one additional year. Interpreting that ratio as a <causal effect> needs more than a good fit: prior attainment or family circumstances may affect both schooling and earnings, creating <confounding>. <Randomization>, suitable <instrumental variables> or a credible natural experiment can support causal interpretation under additional assumptions. The numerical regression coefficient alone supplies an association within its model, not those assumptions.
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