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 VERY BRIEF OVERVIEW 2023**

This 29 slide overview was presented October
2023

machlearn2019_Intro_very_brief.pdf

**BOOK CHAPTER: 2022**

Chapter 28 in A. Colin Cameron and Pravin K.
Trivedi, **Microeconometrics using Stata: Volume 2 Nonlinear
Models and Causal Inference Methods** covers Machine
Learning Methods for Prediction and for Causal Inference. **Click here**
for book information.

Stata mostly uses the Lasso, ridge regression and
elastic net. This is enough to provide a good introduction to
machine learning methods. Additionally Stata has some built-in
commands for causal inference using the LASSO in the partial
linear model and the standard binary treatment effects model.

For other machine learners such as neural nets and random forests it is standard to use packages in Python or R.

**SHORT COURSE: 2024**

In Spring 2024 I spent five weeks on machine
learning in my ECN 240F class.

The slides are an updated version of eight hours of accelerated
lectures on machine learning for econometrics I gave in May 2022
at Simon Fraser University.

**Click here**
for course slides (updated to 2024), programs and data sets.

This 60 slide overview was presented June 2019

**machlearn2019_Intro_brief.pdf**

**SLIDES: CAUSAL MACHINE LEARNING FOR ECONOMICS BRIEF OVERVIEW
2020**

For statistical learning a leading text is the undergraduate
/ masters level book

**ISL2: **Gareth James, Daniela Witten, Trevor Hastie and
Robert Tibshirani (2021), **An Introduction to Statistical
Learning: with Applications in R**, Second Edition,
Springer.

A free legal
pdf is at https://www.statlearning.com/

A Python
version of this book is also available.

**ISLP:** Garetha James, Daniela Witten, Trevor
Hastie, Robert Tibsharani and Jonathan Taylor,
(2023), **An Introduction to Statistical Learning:
With Applications in Python**, Springer.

A
free legal pdf is at https://www.statlearning.com/

Supplementary material on statistical learning is in 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)

USEFUL TEXTS FOR MACHINE LEARNING (FOR ECONOMICS)

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

This is a very active area. The papers listed below were published between 2011 and 2019.

Machine learning prediction in economics

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.

In Spring 2023 I used Python for machine learning at a very introductory level.