Contents

Textbooks in econometrics and machine learning

Textbooks in econometrics and machine learning

This list target free and open textbooks (as a website or legal free PDF). Earlier and foundational books also mentioned.

Causal inference

Open:

Earlier:

Econometrics

Open:

Earlier:

  • Peter Kennedy [Ken08] (very appealing to intuition)

  • Stock and Watson [SW11] (expensive when buying new)

Foundational:

  • Hamilton on time series [Ham94]

Probability

Open:

Earlier:

Bibliography

AP09

Joshua David Angrist and Jörn-Steffen Pischke. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press, Princeton, 2009.

AI19

Susan Athey and Guido W. Imbens. Machine Learning Methods That Economists Should Know About. Annual Review of Economics, 11(1):685–725, aug 2019.

BH15

Joseph K. Blitzstein and Jessica Hwang. Introduction to Probability. Texts in statistical science. CRC Press/Taylor & Francis Group, Boca Raton, 2015.

Cun21

Scott Cunningham. Causal Inference: The Mixtape. Yale University Press, New Haven\,; London, 2021.

Dek05

Michel Dekking, editor. A Modern Introduction to Probability and Statistics: Understanding Why and How. Springer texts in statistics. Springer, London, 2005.

Dow21

Allen B. Downey. Think Bayes: Bayesian statistics in Python. O’Reilly, Beijing Boston Farnham Sebastopol Tokyo, second edition edition, 2021.

Gel14

Andrew Gelman. Bayesian Data Analysis. Chapman & Hall/CRC texts in statistical science. CRC Press, Boca Raton, third edition edition, 2014.

GHV21

Andrew Gelman, Jennifer Hill, and Aki Vehtari. Regression and other stories. Analytical methods for social research. Cambridge University Press, Cambridge New York, NY Port Melbourne, VIC New Delhi Singapore, 2021.

Geron19

Aurélien Géron. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media, Inc, Beijing [China]\,; Sebastopol, CA, second edition edition, 2019.

Ham94

James D. Hamilton. Time Series Analysis. Princeton University Press, Princeton, N.J, 1994.

HK21

Nick Huntington-Klein. The Effect: An Introduction to Research Design and Causality. Chapman and Hall/CRC Press, Boca Raton, 2021.

IR15

Guido Imbens and Donald B. Rubin. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press, New York, 2015.

JWHT21

Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An introduction to statistical learning: with applications in R. Springer texts in statistics. Springer, New York, second edition edition, 2021.

JB03

E. T Jaynes and G. Larry Bretthorst. Probability theory: the logic of science. Cambridge University Press, 2003.

Ken08

Peter Kennedy. A Guide to Econometrics. Blackwell Pub, Malden, MA, 6th ed edition, 2008.

Kur19

Will Kurt. Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks. No Starch Press, San Francisco, 2019.

Mar22

Osvaldo Martin. Bayesian Modeling and Computation in Python. Texts in statistical science. CRC Press, Boca Raton, 2022.

McE20

Richard McElreath. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. CRC texts in statistical science. Taylor and Francis, CRC Press, Boca Raton, 2 edition, 2020.

PM18

Judea Pearl and Dana Mackenzie. The Book of Why: The New Science of Cause and Effect. Basic Books, New York, 2018.

SW11

James H. Stock and Mark W. Watson. Introduction to Econometrics. The Addison-Wesley series in economics. Addison-Wesley, Boston, 3rd ed edition, 2011.

Var14

Hal R. Varian. Big Data: New Tricks for Econometrics. Journal of Economic Perspectives, 28(2):3–28, may 2014.