MACHINE LEARNING or STATISTICAL LEARNING
 
Colin Cameron, Department of Economics, University of California - Davis  June 2019

Machine learning methods for prediction are well-established in the statistical and computer science literature.
Applying machine learning methods for causal influence is a very active area in the economics literature.
A summary such as that in the slides below can become dated very quickly. 

SLIDES: MACHINE LEARNING BRIEF OVERVIEW

This 60 slide overview was presented June 2019
machlearn2019_Intro_brief.pdf

SLIDES: MORE DETAIL ON MACHINE LEARNING IN GENERAL

The following two sets of slides provide much more detail on basic machine learning methods.
They were created in April 2019 for short courses in Germany
machlearn2019_part1.pdf   (Basics: selection, shrinkage, dimension reduction, LASSO)
machlearn2019_part2.pdf   (Flexible methods: including random forests, classification and cluster analysis)

SLIDES: MORE DETAIL ON MACHINE LEARNING FOR ECONOMICS

The following set of slides provides much more detail on use in economics of machine learning methods.
These slides were created in April 2019 for short courses in Germany and presentation at U.C. Riverside.
They cover a prediction example in economics and then various methods for causal inference in the partially linear model and in heterogeneous effects models.
The slides also list key references in the current economics literature.
machlearn2019_Riverside_2.pdf

USEFUL TEXTS FOR MACHINE LEARNING (NOT ECONOMICS)

For statistical learning the main text used in 240F is an undergraduate / masters level book
ISL: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibsharani (2013), An Introduction to Statistical Learning: with Applications in R, Springer.
A free legal pdf is at http://www-bcf.usc.edu/~gareth/ISL/ and a $25 hardcopy can be obtained via http://www.springer.com/gp/products/books/mycopy

Supplementary material on statistical learning came from the Ph.D. level book
ESL: Trevor Hastie, Robert Tibsharani and Jerome Friedman (2009), The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer.
A free legal pdf is at http://statweb.stanford.edu/~tibs/ElemStatLearn/index.html and a $25 hardcopy can be obtained via http://www.springer.com/gp/products/books/mycopy

A newer book that is good but I haven't used is
Bradley Efron and Trevor Hastie (2016)
Computer Age Statistical Inference: Algorithms, Evidence and Data Science,  Cambridge University Press.

LEADERS IN ECONOMETRICS

Bringing established machine learning methods into econometrics is currently an active area. The literature focuses on valid statistical inference controlling for fist-stage data mining, and causal inference. Leading econometricians include
Victor Chernozhukov    http://web.mit.edu/~vchern/www/
https://faculty.fuqua.duke.edu/~abn5/belloni-index.html
Alex Belloni https://faculty.fuqua.duke.edu/~abn5/belloni-index.html
Christian Hansen http://faculty.chicagobooth.edu/christian.hansen/research/
Susan Athey  https://www.gsb.stanford.edu/faculty-research/faculty/susan-athey   https://people.stanford.edu/athey/research
Guido Imbens    https://www.gsb.stanford.edu/faculty-research/faculty/guido-w-imbens  https://people.stanford.edu/imbens/publications

ONLINE COURSES

Coursera has many courses   https://www.coursera.org/browse/data-science/machine-learning?languages=en

SOME ECONOMICS REFERENCES
This is a very active area: All the papers below were published in 2011 or later.

Machine learning prediction in economics
Hal Varian (2014), "Big Data: New Tricks for Econometrics", Journal of Economic Perspectives, Spring, 3-28.
Sendhil Mullainathan and J. Spiess: "Machine Learning: An Applied Econometric Approach", Journal of Economic Perspectives, Spring 2017, 87-106.
Jon Kleinberg, H. Lakkaraju, Jure Leskovec, Jens Ludwig, Sendhil Mullainathan (2018), "Human decisions and Machine Predictions", Quarterly Journal of Economics, 237-293.

Surveys of causal inference in economics
Susan Athey (2018), "The Impact of Machine Learning on Economics". http://www.nber.org/chapters/c14009.pdf
Susan Athey and Guido Imbens (2019), "Machine Learning Methods Economists Should Know About."
Alex Belloni, Victor Chernozhukov and Christian Hansen (2014), "High-dimensional methods and inference on structural and treatment effects," Journal of Economic Perspectives, Spring, 29-50. 

Causal inference in economics
Alex Belloni, Victor Chernozhukov and Christian Hansen (2011), "Inference Methods for High-Dimensional Sparse Econometric Models," Advances in Economics and Econometrics, ES World Congress 2010, ArXiv 2011.
Alex Belloni, D. Chen, Victor Chernozhukov and Christian Hansen (2012), "Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain", Econometrica, Vol. 80, 2369-2429.
Alex Belloni, Victor Chernozhukov, Ivan Fernandez-Val and Christian Hansen (2017), "Program Evaluation and Causal Inference with High-Dimensional Data," Econometrica, 233-299.
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey and James Robins (2018), "Double/debiased machine learning for treatment and structural parameters," The Econometrics Journal, 21, C1-C68.
Max Farrell (2015), "Robust Estimation of Average Treatment Effect with Possibly more Covariates than Observations", Journal of Econometrics, 189, 1-23.
Max Farrell, Tengyuan Liang and Sanjog Misra (2018), "Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands," arXiv:1809.09953v2.
Stefan Wager and Susan Athey (2018), "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," JASA, 1228-1242.

Stata Software
Achim Ahrens, Christian Hansen, Mark Schaffer (2019), "lassopack: Model selection and prediction with regularized regression in Stata," arXiv:1901.05397