Machine Learning and Econometric’s whats the difference?

Reading some of the online links related to interview questions for data scientist position , i stumbled upon a question – what is the difference between Machine Learning and Econometrics? Coming from economics background one always feels that there is a lot of similarity between the two but the discussion below provides a good overview

Varian(2014), Diebold(2016) and Mullainathan & Spiess (2017) have written blog posts and articles which discuss this question in detail. Following posts discusses a general theme and readers curious to learn more should definitely read the articles referenced here.

  1. Machine learning is mostly concerned with both summarizing data and prediction (i.e \widehat{y} ) whereas Econometrics can be broken down into four main categories prediction, summarization, estimation and inference.
  2. Machine learning is concerned with non-causal prediction whereas in econometrics we are ore concerned with causal prediction. Diebold(2016) in his posts explains machine learning non-causal prediction as “if a new person \mathrm{\textit{i}}   arrives with covariates \ {X_i} what is my minimum – Mean Squared Error guess of \ {y_i} ? ”  whereas causal prediction is more concerned with, if i intervene and give a person \mathrm{\textit{i}} a certain treatment what is my minimum- Mean Squared Error guess of  \Delta {y_i} ?
  3. Machine learning often uses the terms features or predictors which in econometrics is known as independent variables.


  1. Varian, Hal R. 2014. “Big Data: New Tricks for Econometrics.” Journal of Economic Perspectives28 (2): 3-28.  Can be downloaded here.
  2. Diebold :
  3. Mullainathan, Sendhil, and Jann Spiess. 2017. “Machine Learning: An Applied Econometric Approach.”Journal of Economic Perspectives31 (2): 87-106




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