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We discuss the relevance of the recent Machine Learning (ML) literature for economics and econometrics.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R Thompson · 1933
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Distributional structure
Z. S. Harris · 1954
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A synopsis of linguistic theory 1930-1955
J. R. Firth · 1957
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Ridge regression: Biased estimation for nonorthogonal problems
Arthur E Hoerl and Robert W Kennard · 1970
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Algorithm as 136: A k-means clustering algorithm
John A Hartigan and Manchek A Wong · 1979
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Parametric empirical bayes inference: theory and applications
Carl N Morris · 1983
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The central role of the propensity score in observational studies for causal effects
Paul R Rosenbaum and Donald B Rubin · 1983
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Classification and Regression Trees
Leo Breiman, Jerome Friedman, Charles J Stone, and Richard A Olshen · 1984
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Asymptotically efficient adaptive allocation rules
Tze Leung Lai and Herbert Robbins · 1985
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Statistics and causal inference
Paul W Holland · 1986
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Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
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Kernel estimators of regression functions
Herman J Bierens · 1987
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Local likelihood estimation
Robert Tibshirani and Trevor Hastie · 1987
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Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
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Artificial neural networks: approximation and learning theory
Halbert White · 1992
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Better subset selection using the non-negative garotte
L Breiman · 1993
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Restrictions of economic theory in nonparametric methods
Rosa L Matzkin · 1994
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Support-vector networks
Corinna Cortes and Vladimir Vapnik · 1995
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Semiparametric efficiency in multivariate regression models with missing data
James Robins and Andrea Rotnitzky · 1995
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Bagging predictors
Leo Breiman · 1996
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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Efficient and adaptive estimation for semiparametric models
Peter Bickel, Chris Klaassen, Yakov Ritov, and Jon Wellner · 1998
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Online learning and stochastic approximations
Léon Bottou · 1998
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Reinforcement learning: An introduction
Richard S Sutton, Andrew G Barto, et al · 1998
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Statistical learning theory , volume 1
Vladimir Naumovich Vapnik · 1998
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Econometrics and decision theory
Gary Chamberlain · 2000
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Ensemble methods in machine learning
Thomas G Dietterich · 2000
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Econometric analysis 4th edition
William H Greene · 2000
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Asymptotic Statistics
Aad W Van der Vaart · 2000
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Learning with kernels: support vector machines, regularization, optimization, and beyond
Bernhard Scholkopf and Alexander J Smola · 2001
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Determining the number of factors in approximate factor models
Jushan Bai and Serena Ng · 2002
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Stochastic gradient boosting
Jerome H Friedman · 2002
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Subset selection in regression
Alan Miller · 2002
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Inferential theory for factor models of large dimensions
Jushan Bai · 2003
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A neural probabilistic language model
Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin · 2003
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Least angle regression
Bradley Efron, Trevor Hastie, Iain Johnstone, Robert Tibshirani, et al · 2004
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Regularization and variable selection via the elastic net
Hui Zou and Trevor Hastie · 2005
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Neural probabilistic language models
Y. Bengio, H. Schwenk, J.-S. Senécal, F. Morin, and J.-L. Gauvain · 2006
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Targeted maximum likelihood learning
Mark J van der Laan and Daniel Rubin · 2006
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Lessons from the netflix prize challenge
Robert M Bell and Yehuda Koren · 2007
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The netflix prize
James Bennett, Stan Lanning, et al · 2007
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The dantzig selector: Statistical estimation when p p is much larger than n n
Emmanuel Candès and Terence Tao · 2007
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Large sample sieve estimation of semi-nonparametric models
Xiaohong Chen · 2007
Cited alongside, same era.
Nonparametric identification
Rosa L Matzkin · 2007
Cited alongside, same era.
Relaxed lasso
Nicolai Meinshausen · 2007
Cited alongside, same era.
Three new graphical models for statistical language modelling
A. Mnih and G. E. Hinton · 2007
Cited alongside, same era.
Mostly harmless econometrics: An empiricist’s companion
Joshua D Angrist and Jörn-Steffen Pischke · 2008
Cited alongside, same era.
Regression discontinuity designs: A guide to practice
Guido W Imbens and Thomas Lemieux · 2008
Cited alongside, same era.
Top 10 algorithms in data mining
Scalable recommendation with hierarchical Poisson factorization
P. Gopalan, J. Hofman, and D. M. Blei · 2015
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Statistical Learning with Sparsity: The Lasso and Generalizations
Trevor Hastie, Robert Tibshirani, and Martin Wainwright · 2015
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Causal Inference in Statistics, Social, and Biomedical Sciences
Guido W Imbens and Donald B Rubin · 2015
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Who should be treated? Empirical welfare maximization methods for treatment choice
Toru Kitagawa and Aleksey Tetenov · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Batch learning from logged bandit feedback through counterfactual risk minimization
A. Swaminathan and T. Joachims · 2015
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Xindong Wu, Vipin Kumar, J Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J McLachlan, Angus Ng, Bing Liu, S Yu Philip, et al · 2008
Cited alongside, same era.
Model-based recursive partitioning
Achim Zeileis, Torsten Hothorn, and Kurt Hornik · 2008
Cited alongside, same era.
Introduction to machine learning
Ethem Alpaydin · 2009
Cited alongside, same era.
Topic models
David M Blei and John D Lafferty · 2009
Cited alongside, same era.
Exact matrix completion via convex optimization
Emmanuel J Candès and Benjamin Recht · 2009
Cited alongside, same era.
The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
Cited alongside, same era.
Word representations via Gaussian embedding
L. Vilnis and A. McCallum · 2015
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Stable weights that balance covariates for estimation with incomplete outcome data
Jose R. Zubizarreta · 2015
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RAND-WALK: A latent variable model approach to word embeddings
S. Arora, Y. Li, Y. Liang, and T. Ma · 2016
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Recursive partitioning for heterogeneous causal effects
Susan Athey and Guido Imbens · 2016
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Bayesian neural word embedding
O. Barkan · 2016
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Best subset selection via a modern optimization lens
Dimitris Bertsimas, Angela King, Rahul Mazumder, et al · 2016
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Balancing, regression, difference-in-differences and synthetic control methods: A synthesis
Nikolay Doudchenko and Guido W Imbens · 2016
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Computer Age Statistical Inference , volume 5
Bradley Efron and Trevor Hastie · 2016
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Counterfactual Prediction with Deep Instrumental Variables Networks
Jason Hartford, Greg Lewis, and Matt Taddy · 2016
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Doubly robust off-policy value evaluation for reinforcement learning
N. Jiang and L. Li · 2016
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Data-efficient off-policy policy evaluation for reinforcement learning
P. Thomas and E. Brunskill · 2016
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Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou · 2017
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Beyond prediction: Using big data for policy problems
Susan Athey · 2017
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Susan Athey and Stefan Wager · 2017
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Principal components and regularized estimation of factor models
Jushan Bai and Serena Ng · 2017
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Dynamic word embeddings via skip-gram filtering
R. Bamler and S. Mandt · 2017
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Double/debiased/neyman machine learning of treatment effects
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, and Whitney Newey · 2017
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Estimation considerations in contextual bandits
M. Dimakopoulou, S. Athey, and G. Imbens · 2017
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Text as data
Matthew Gentzkow, Bryan T Kelly, and Matt Taddy · 2017
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Extended comparisons of best subset selection, forward stepwise selection, and the lasso
Trevor Hastie, Robert Tibshirani, and Ryan J Tibshirani · 2017
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Balanced policy evaluation and learning
N. Kallus · 2017
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Meta-learners for estimating heterogeneous treatment effects using machine learning
Sören Künzel, Jasjeet Sekhon, Peter Bickel, and Bin Yu · 2017
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Machine learning: an applied econometric approach
Sendhil Mullainathan and Jann Spiess · 2017
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Shopper: A probabilistic model of consumer choice with substitutes and complements
Francisco JR Ruiz, Susan Athey, and David M Blei · 2017
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Estimation and inference of heterogeneous treatment effects using random forests
Stefan Wager and Susan Athey · 2017
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Modeling consumer preferences and price sensitivities from large-scale grocery shopping transaction logs
M. Wan, D. Wang, M. Goldman, M. Taddy, J. Rao, J. Liu, D. Lymberopoulos, and J. McAuley · 2017
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Econometric methods for program evaluation
Alberto Abadie and Matias D Cattaneo · 2018
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The impact of machine learning on economics
Susan Athey · 2018
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Balanced linear contextual bandits
Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey, and Guido Imbens · 2018
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Max H Farrell, Tengyuan Liang, and Sanjog Misra · 2018
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Rina Friedberg, Julie Tibshirani, Susan Athey, and Stefan Wager · 2018
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Machine learning: a concise introduction , volume 285
Steven W Knox · 2018
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Orthogonal ML for demand estimation: High dimensional causal inference in dynamic panels
V. Semenova, M. Goldman, V. Chernozhukov, and M. Taddy · 2018
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Offline multi-action policy learning: Generalization and optimization
Zhengyuan Zhou, Susan Athey, and Stefan Wager · 2018
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Ensemble methods for causal effects in panel data settings
Susan Athey, Mohsen Bayati, Guido Imbens, and Qu Zhaonan · 2019
Closest in time.
The Hundred-page Machine Learning Book
Andriy Burkov · 2019
Closest in time.