What’s the Difference Between Econometrics and Data Science?

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What's the Difference Between Econometrics and Data Science?

What’s the difference between econometrics and data science? I would say that  the principal difference is the approach  to the problem of prediction. Data scientists are often concerned with curve-fitting type  approaches to prediction. So any model that fits the data well will do.

If it’s past experience, we might  be interested in using that to extrapolate to the future. A lot of the data science  agenda is tied to somebody’s marketing problems. You’re trying to figure out who will buy something, who will take some action. Econometrics, in my view, deals with kind of a harder class of problems.

Econometricians are more concerned  with causal relationships. In other words,  if we manipulate something, say, health insurance  or monetary policy, what’s the world going to look like  in response to that change? We don’t take it for granted that  the past is a good guide to that because we understand that variation and variable is associated with lots of potential  confounding variables — we would say other things  that are moving that also perhaps affect outcomes.

The simple observed relationship there is often misleading because there are factors that are not well controlled, and we have in mind that there is a research design that involves more  than curve fitting. In fact, we’re fairly indifferent to curve fitting in economics. I think we want to know, for example, whether it matters if you go to an expensive  private college — does that change your life course in the form of higher earnings?

That’s not really  a curve-fitting question, that’s a causal question. Ready  to master econometrics?

 

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