NOTE: The following list of references and links may be useful. However, note that they do not necessarily cover all the material we plan to cover in the class.
Athey, S. (2018). The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda. University of Chicago Press.
Athey, S., & Imbens, G. W. (2017). The state of applied econometrics: Causality and policy evaluation. Journal of Economic Perspectives, 31(2), 3-32.
Belloni, A., Chernozhukov, V., & Hansen, C. (2014). High-dimensional methods and inference on structural and treatment effects. Journal of Economic Perspectives, 28(2), 29-50.
Mullainathan, S., & Spiess, J. (2017). Machine learning: an applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106.
Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3-28.
AEA 2018 Continuing Education, “Machine Learning and Econometrics” (Athey and Imbens).
NBER 2015 Method Lectures, “Lectures on Machine Learning” (Athey and Imbens).
NBER 2013 Method Lectures, “Econometric Methods for High-Dimensional Data” (Chernozhukov, Gentzkow, Hansen, Shapiro, Taddy).
“Machine Learning and Prediction in Economics and Finance”, Sendhil Mullainathan, AFA Lecture, 2017.
“The Impact of Machine Learning on Econometrics and Economics”, Susan Athey, AEA Lecture, 2019.
NOTE: To the best of our knowledge, there is no “Machine Learning for Economists” textbook out there yet (Though there is one we know of that is in the making, co-authored by Mullainathan and Spiess.)
An Introduction to Statistical Learning with Applications in R
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
This book provides a hands-on and R-based introduction to Machine Learning.
PDF available online
Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani and Jerome Friedman
This book covers the same topics as previous book (and more), however, it provides more rigorous treatment.
PDF available online
Machine Learning - A Probablistic Prespective
Kevin P. Murphy
This book includes basic topics in statistical modeling, as well as advanced machine learning topics. It comes with Matlab code to reproduce almost every figure and algorithm, discussed in the book.
The following two books presents a detailed account of recently developed approaches to estimating models containing a large number of parameters, including the Lasso and versions of it:
Statistical Learning with Sparsity - The Lasso and Generalizations
Trevor Hastie, Robert Tibshirani, and Martin Wainwright
In book contains an introduction to and a summary of the actively developing field of statistical learning with sparse models.
PDF available online
Statistics for High-Dimensional Data - Methods, Theory and Applications
Peter Buhlmann, and Sara van de Geer
This book brings together methodological concepts, computational algorithms, a few applications and mathematical theory for high-dimensional statistics.
The following two textbooks provide a graduate level treatment of causal inference in social sciences:
Mostly Harmless Econometrics
Joshua Angrist and Jorn-Steffen Pischke
Causal Inference for Statistics, Social, and Biomedical Sciences
Guido Imbens and Donald Rubin
Text Mining with R - A Tidy Approach
Julia Silge and David Robinson
The go-to textbook for those interested in textmining with R.
Prediction Machines
Ajay Agrawal, Joshua Gans, Avi Goldfarb
A must-read book about the economics of AI. These authors recast the rise of AI as a drop in the cost of prediction and show how basic tools from IO economics can help analyze the effects of AI on the economy and our society.
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
Cathy O’Neil
In this book, O’neil discusses the dangers of using on black-box descion making algorithms that are prone to significant biases in tasks like granting (or denying) loans, workers evaluation, parole setting, health monitoring.
Big Data: Does Size Matter?
Timandra Harkness
A non-technical and fun introduction to big-data.
The Book of Why: The New Science of Cause and Effect
Judea Pearl and Dana Mackenzie Pearl and Mackenzie’s book is a must for anyone interested in causality. It lays the foundations of Pearl’s approach to causal inference in plain English and graphs with very few equations.
Susan Athey (Stanford)
Alexandre Belloni (Duke)
Victor Chernozhukov (MIT)
Francis Diebold (Penn)
Christian Hansen (Chicago)
Guido Imbens (Stanford)
Maximilian Kasy (Harvard)
Grant McDermott (University of Oregon)
Sendhil Mullainathan (Chicago)
Jann Spiess (Harvard)
Stefan Wager (Stanford)
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