Not long ago Tyler Cowen blogged at Marginal Revolution about a Quora post by Susan Athey discussing the impact of machine learning on econometrics, flavors of machine learning, and differences in the emphasis placed on tools and methodologies traditional in each field. The differences often hinge on whether one's intention is to explain or predict, or if one is interested in causal inference vs analytics. I really liked the point about instrumental variables made in the snippet below:
"Yet, a cornerstone of introductory econometrics is that prediction is not causal inference, and indeed a classic economic example is that in many economic datasets, price and quantity are positively correlated. Firms set prices higher in high-income cities where consumers buy more; they raise prices in anticipation of times of peak demand. A large body of econometric research seeks to REDUCE the goodness of fit of a model in order to estimate the causal effect of, say, changing prices. If prices and quantities are positively correlated in the data, any model that estimates the true causal effect (quantity goes down if you change price) will not do as good a job fitting the data….Techniques like instrumental variables seek to use only some of the information that is in the data – the “clean” or “exogenous” or “experiment-like” variation in price—sacrificing predictive accuracy in the current environment to learn about a more fundamental relationship that will help make decisions about changing price. This type of model has not received almost any attention in ML."
Tyler also points to a wealth of resources by Suan Athey here. And check out the mini-course she taught with Guido Imbens via NBER.
The differences and synergies between tools used in both econometrics and machine learning is something I have been interested in for a long time and have blogged about several times in the past. Kenneth Sanford and Hal Varian have also been writing about this as well. See related content below.
Related Content and Further Reading
Economists as Data Scientists http://econometricsense.blogspot.com/2012/10/economists-as-data-scientists.html
Econometrics, Math, and Machine Learning….what? http://econometricsense.blogspot.com/2015/09/econometrics-math-and-machine.html
"Mathematical Themes in Economics, Machine Learning, and Bioinformatics" (2010)
Available at: http://works.bepress.com/matt_bogard/7/
Notes to 'Support' an Understanding of Support Vector Machines http://econometricsense.blogspot.com/2012/05/notes-to-support-understanding-of.html
Culture War: Classical Statistics vs. Machine Learning http://econometricsense.blogspot.com/2011/01/classical-statistics-vs-machine.html
Analytics vs Causal Inference http://econometricsense.blogspot.com/2014/01/analytics-vs-causal-inference.html
Big Data: Don’t throw the baby out with the bath water http://econometricsense.blogspot.com/2014/05/big-data-dont-throw-baby-out-with.html
To Explain or Predict http://econometricsense.blogspot.com/2015/03/to-explain-or-predict.html
Big Data: Causality and Local Expertise Are Key in Agronomic Applications http://econometricsense.blogspot.com/2014/05/big-data-think-global-act-local-when-it.html
Big Data: New Tricks for Econometrics
Hal R. Varian
Revised: April 14, 2014 http://people.ischool.berkeley.edu/~hal/Papers/2013/ml.pdf
Is machine learning trending with economists? (Kenneth Sanford) http://blogs.sas.com/content/subconsciousmusings/2015/06/05/is-machine-learning-trending-with-economists/