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.

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: CAUSAL MACHINE LEARNING FOR ECONOMICS BRIEF
OVERVIEW
**

**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)

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
**

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

Bradley Efron and Trevor Hastie (2016)

The following book is more recent and includes some causal methods

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

Jon Kleinberg, H. Lakkaraju, Jure Leskovec, Jens Ludwig, Sendhil Mullainathan (2018), "Human Decisions and Machine Predictions", Quarterly Journal of Economics, 237-293.

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.

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 version 16 introduced commands for lasso, ridge, elasticnet and casual inference in the partial linear and related models with exogenous or endogenous regressors.

The following Stata add-on will work with Stata 16 and also with earlier versions of Stata

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