This also raises the risk that the algorithms are applied naively or their output is misinterpreted. Machine learning algorithms are now technically easy to use: you can download convenient packages in R or Python. So applying machine learning to economics requires finding relevant tasks. Specifically, machine learning revolves around the problem of prediction, while many economic applications revolve around parameter estimation. Machine learning not only provides new tools, it solves a different problem. This similarity to econometrics raises questions: How do these new empirical tools fit with what we know? As empirical economists, how can we use them? We present a way of thinking about machine learning that gives it its own place in the econometric toolbox. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the presence y of a face from pixels x. Machines are increasingly doing "intelligent" things.
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