I posted Susan Athey’s 2019 Luncheon address to the AEA and AFA in part 1 of this post. (See here for her address).
Now, in part 2, I post details on the continuing education Course on Machine Learning and Econometrics from 2018 featuring the joint efforts of Susan Athey and Guido Imbens.
It relates to the basis for this blog where I advocate for economists in Data Science roles. As a crude overview, ML uses many of the techniques known by all econometricians and has grown out of data mining and exploratory data analysis, long avoided by economists precisely because such methods lead to in the words of Jan Kmenta “beating the data until it confesses.” ML also makes use of AI type methods that started with brute force methods noting that the computer can be set lose on a data set and in a brutish way try all possible models and predictions seeking a best statistical fit, but not necessarily the best economic fit since the methods ignore both causality and the ability to interpret the results with a good explanation.
Economists historically have focused on models that are causal and provide the ability to focus on the explanation, the the ability to say why. Their model techniques are designed to test economic hypotheses about the problem and not just to get a good fit.
To say we have discussed historically the opposite ends of a fair coin by setting up the effect of X–>y is not too far off. ML focuses on y and econometrics focus on X. The future is focusing on both, the need to focus good algos on what is y and the critical understanding of “why” which is the understanding of the importance of X.
This course offered by the American Economic Association just about one year ago, represents the state of the art of the merger of ML and econometrics. I offer it here (although you can go directly to the AEA website) so more can explore how economists need to incorporate the lessons of ML and of econometrics and help produce even stronger data science professionals.
AEA Continuing Education Short Course: Machine Learning and Econometrics, Jan 2018
Course Presenters
Susan Athey is the Economics of Technology Professor at Stanford Graduate School of Business. She received her bachelor’s degree from Duke University and her PhD from Stanford. She previously taught at the economics departments at MIT, Stanford and Harvard. Her current research focuses on the intersection of econometrics and machine learning. As one of the first “tech economists,” she served as consulting chief economist for Microsoft Corporation for six years.
Guido Imbens is Professor of Economics at the Stanford Graduate School of Business. After graduating from Brown University Guido taught at Harvard University, UCLA, and UC Berkeley. He joined the GSB in 2012. Imbens specializes in econometrics, and in particular methods for drawing causal inferences. Guido Imbens is a fellow of the Econometric Society and the American Academy of Arts and Sciences. Guido Imbens has taught in the continuing education program previously in 2009 and 2012.
Two day course in nine parts - Machine Learning and Econometrics, Jan 2018
Materials:
Course Materials (will attach to your Google Drive)
The syllabus is included in the course materials and carries links to 4 pages of readings which are copied and linked to the source articles below.
Webcasts:
View Part 1 – Sunday 4.00-6.00pm: Introduction to Machine Learning Concepts
(a) S. Athey (2018, January) “The Impact of Machine Learning on Economics,” Sections 1-2.
(b) H. R. Varian (2014) “Big data: New tricks for econometrics.” The Journal of Economic Perspectives, 28 (2):3-27.
(c) S. Mullainathan and J. Spiess (2017) “Machine learning: an applied econometric approach” Journal of Economic Perspectives, 31(2):87-106
View Part 2 – Monday 8.15-9.45am: Prediction Policy Problems
(a) S. Athey (2018, January) “The Impact of Machine Learning on Economics,” Section 3.
(b) S. Mullainathan and J. Spiess (2017) “Machine learning: an applied econometric approach.” Journal of Economic Perspectives, 31(2):87-106.
View Part 3 – Monday 10.00-11.45am: Causal Inference: Average Treatment Effects
(a) S. Athey (2018, January) “The Impact of Machine Learning on Economics,” Section 4.0, 4.1.
(b) A. Belloni, V. Chernozhukov, and C. Hansen (2014) “High-dimensional methods and inference on structural and treatment effects.” The Journal of Economic Perspectives, 28(2):29-50.
(c) V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duo, C. Hansen, W. Newey, and J. Robins (2017, December) “Double/Debiased Machine Learning for Treatment and Causal Parameters.”
(d) S. Athey, G. Imbens, and S.Wager (2016) “Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges.” Forthcoming, Journal of the Royal Statistical Society-Series B.
View Part 4 – Monday 12.45-2.15pm: Causal Inference: Heterogeneous Treatment Effects
(a) S. Athey (2018, January) “The Impact of Machine Learning on Economics,” Section 4.2.
(b) S. Athey, G. Imbens (2016) “Recursive partitioning for heterogeneous causal effects.” Proceedings of the National Academy of Sciences, 113(27), 7353-7360.
(a) S. Athey (2018, January) “The Impact of Machine Learning on Economics,” Section 4.2, 4.4.
(b) S. Athey, and G. Imbens (2017) “The State of Applied Econometrics: Causality and Policy Evaluation,” Journal of Economic Perspectives, vol 31(2):3-32.
(c) S. Wager and S. Athey (2017) “Estimation and inference of heterogeneous treatment effects using random forests.” Journal of the American Statistical Association
(d) S. Athey, Tibshirani, J., and S.Wager (2017, July) “Generalized Random Forests“
(e) S. Athey, and Imbens, G. (2015) “A measure of robustness to misspeci cation.” The American Economic Review, 105(5), 476-480.
View Part 6 – Monday 4.15-5.15pm: Causal Inference: Optimal Policies and Bandits
(a) S. Athey. (2018, January) “The Impact of Machine Learning on Economics,”
Section 4.3.
(b) S. Athey and S. Wager (2017) “Efficient Policy Learning.”
(c) M. Dudik, D. Erhan, J. Langford, and L. Li, (2014) “Doubly Robust Policy
Evaluation and Optimization” Statistical Science, Vol 29(4):485-511.
(d) S. Scott (2010), “A modern Bayesian look at the multi-armed bandit,” Applied Stochastic Models in Business and Industry, vol 26(6):639-658.
(e) M. Dimakopoulou, S. Athey, and G. Imbens (2017). “Estimation Considerations in Contextual Bandits.”
View Part 7 – Tuesday 8.00-9.15am: Deep Learning Methods
(a) Y. LeCun, Y. Bengio and G. Hinton, (2015) “Deep learning” Nature, Vol. 521(7553): 436-444.
(b) I. Goodfellow, Y. Bengio, and A. Courville (2016) “Deep Learning.” MIT Press.
(c) J. Hartford, G. Lewis, K. Leyton-Brown, and M. Taddy (2016) Counterfactual
Prediction with Deep Instrumental Variables Networks.”
View Part 8 – Tuesday 9.30-10.45am: Classi cation
(a) L. Breiman, J. Friedman, C. J. Stone R. A. Olshen (1984) “Classi cation and
regression trees,” CRC press.
(b) I. Goodfellow, Y. Bengio, and A. Courville (2016) \Deep Learning.” MIT Press.
View Part 9 – Tuesday 11.00am-12.00pm: Matrix Completion Methods for Causal Panel Data Models
(a) S. Athey, M. Bayati, N. Doudchenko, G. Imbens, and K. Khosravi (2017) “Matrix Completion Methods for Causal Panel Data Models.”
(b) J. Bai (2009), “Panel data models with interactive fi xed effects.” Econometrica, 77(4): 1229{1279.
(c) E. Candes and B. Recht (2009) “Exact matrix completion via convex optimization.” Foundations of Computational Mathematics, 9(6):717-730.