Tractable Contextual Bandits Beyond Realizability |
Sanath Kumar Krishnamurthy, Vitor Hadad |
arXiv:2010.13013
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October 25, 2020
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Local Linear Forests |
Rina Friedberg, Julie Tibshirani, Stefan Wager |
Forthcoming, Journal of Computational and Graphical Statistics.
arXiv preprint: arXiv.org/abs/1807.11408
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September 2020
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Policy Learning with Observational Data (previously titled “Efficient Policy Learning”) |
Stefan Wager |
Forthcoming, Econometrica. arXiv:1702.02896
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February 2017
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Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations |
Guido Imbens, Jonas Metzger, Evan Munro |
Forthcoming, Journal of Econometrics. arXiv:1909.02210
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September 2019
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Combining Experimental and Observational Data to Estimate Treatment Effects on Long Term Outcomes |
Raj Chetty , Guido Imbens |
arXiv:2006.09676
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June 17, 2020
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Policytree: Policy learning via doubly robust empirical welfare maximization over trees |
Erik Sverdrup, Ayush Kanodia, Zhengyuan Zhou, Stefan Wager |
Journal of Open Source Software 5(50, 2232
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June 22, 2020
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Survey Bandits with Regret Guarantees |
Sanath Kumar Krishnamurthy |
arXiv:2002.09814
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February 23, 2020
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Optimal Experimental Design for Staggered Rollouts |
Ruoxuan Xiong, Mohsen Bayati, Guido Imbens |
arXiv:1911.03764
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November 2019
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Estimating Treatment Effects with Causal Forests: An Application |
Stefan Wager |
Observational Studies 5, September 2019, 21-35
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Sufficient Representations for Categorical Variables |
Jonathan Johannemann, Vitor Hadad, Stefan Wager |
arXiv:1908.09874
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August 2019
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Synthetic Difference In Differences |
Dmitry Arkhangelsky, David A. Hirshberg, Guido W. Imbens, Stefan Wager |
arXiv:1812.09970
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January 31, 2019
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Counterfactual Inference for Consumer Choice Across Many Product Categories |
Rob Donnelly, Francisco R. Ruiz, David Blei |
arXiv:1906.02635
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June 7, 2019
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SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements |
Francisco Ruiz, David Blei |
Annals of Applied Statistics (forthcoming), 2019
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Ensemble Methods for Causal Effects in Panel Data Settings |
Mohsen Bayati, Guido Imbens, Zhaonan Qu |
American Economic Review Papers and Proceedings, May 2019, Vol. 109, pp. 65-70
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Machine Learning Methods Economists Should Know About |
Guido Imbens |
arXiv:1903.10075
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March 24, 2019
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Generalized Random Forests |
Julie Tibshirani, Stefan Wager |
Annals of Statistics, January 2019, Vol. 47, Issue 2; pp. 1148-1178
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Learning in Games with Lossy Feedback |
Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Peter Glynn, Yinyu Ye |
32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada.
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Offline Multi-Action Policy Learning: Generalization and Optimization |
Zhengyuan Zhou, Stefan Wager |
arXiv:1810.04778
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October 10, 2018
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Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption |
Guido Imbens |
arXiv:1808.05293
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August 15, 2018
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Stable Predictions across Unknown Environments |
Kun Kuang, Ruoxuan Xiong, Peng Cui, Bo Li |
KDD 2018: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
arXiv:1806.06270
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June 16, 2018
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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests |
Stefan Wager |
Journal of the American Statistical Association, 2018, 1-15
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Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data |
David Blei, Robert Donnelly, Francisco Ruiz, Tobias Schmidt |
American Economic Review Papers and Proceedings, May 2018: Vol .108, pp. 64-67
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Estimation Considerations in Contextual Bandits |
Maria Dimakopoulou, Guido Imbens |
arXiv:1711.07077
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March 2, 2018
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Approximate residual balancing: debiased inference of average treatment effects in high dimensions |
Guido W. Imbens, Stefan Wager |
Journal of the Royal Statistical Society-Series B, February 2018, Vol 80, Issue 4, pp. 597-623
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The Impact of Machine Learning on Economics |
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Forthcoming in book: The Economics of Artificial Intelligence: An Agenda
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Context Selection for Embedding Models |
Liping Liu, Francisco Ruiz, David Blei |
Neural Information Processing Systems, 2017: 4819-4828
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Structured Embedding Models for Grouped Data |
Maja Rudolph, Francisco Ruiz, David Blei |
Neural Information Processing Systems, 2017: 250-260
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When Should You Adjust Standard Errors for Clustering? |
Alberto Abadie, Guido Imbens, Jeffrey Wooldridge |
arXiv:1710.02926
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October 9, 2017
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Matrix Completion Methods for Causal Panel Data Models |
Mohsen Bayati, Nikolay Doudchenko, Guido Imbens, Khashayar Khosravi |
arXiv:1710.10251
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Slides from NBER Summer Institute, Cambridge, July 27, 2017
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Sampling-based vs. Design-based Uncertainty in Regression Analysis (previously titled: Finite Population Causal Standard Errors) |
Alberto Abadie, Guido W. Imbens, Jeffrey M. Wooldridge |
Econometrica (forthcoming). arXiv:1706.01778v1
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June 6, 2017
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The State of Applied Econometrics: Causality and Policy Evaluation |
Guido Imbens |
Journal of Economic Perspectives, 31(2), Spring 2017: 3-32
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July 2016
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Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges |
Guido Imbens, Thai Pham, Stafan Wager |
American Economic Review, 107(5), May 2017: 278-281
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Exact P-values for Network Interference |
Dean Eckles, Guido W. Imbens |
Journal of the American Statistical Association. 2017.
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The Econometrics of Randomized Experiments |
Guido Imbens |
Handbook of Economic Field Experiments, 1 (2017): 73-140.
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Beyond Prediction: Using Big Data for Policy Problems |
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Science. Feb 03, 2017: Vol. 355, Issue 6324, pp. 483-485
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Feb 2017
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Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index |
Raj Chetty, Guido W. Imbens, Hyunseung Kang |
arXiv:1603.09326
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June 2016
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Recursive Partitioning for Heterogeneous Causal Effects |
Guido W. Imbens |
PNAS, 2016, 113(27):7353-7360; published ahead of print July 5, 2016
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Machine Learning and Causal Inference for Policy Evaluation |
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KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Pages 5-6
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A Measure of Robustness to Misspecification |
Guido W. Imbens |
The American Economic Review, 105(5), May 2015:476-480
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Discrete Choice Models with Multiple Unobserved Choice Characteristics |
Guido W. Imbens |
International Economic Review, 48 (4), November 2007:1159-1192
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Nonparametric Approaches to Auctions |
Philip A. Haile |
Handbook of Econometrics. 2007, Vol. 6, Issue A, Pages 3847–3965
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Identification and Inference in Nonlinear Difference-in-Differences Models |
Guido W. Imbens |
Econometrica, 74(2), March 2006:431-497
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Supplemental Material
Matlab Programs and Documentation for Applying CIC Model and generating tables (check with authors for potential updates and for Stata implementation)
Slides for UCL seminar: Distributions of Treatment Effects in Experimental Settings: Applications of Nonlinear Difference-in-Difference Models
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Empirical Models of Auctions |
Philp A. Haile |
NBER Working Paper No. 12126
Blundell, Richard, Whitney Newey, and Torsten Persson (eds.) Advances in Economics and Econometrics, The- ory and Applications: Ninth World Congress, Volume II. Cambridge: Cambridge University Press, 2006, ch. 1, 1-45
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Identification of Standard Auction Models |
Philip A. Haile |
Econometrica, 70(6), November 2002:2107-2140
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August 2000 September 2001
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An Empirical Framework for Testing Theories About Complementarity in Organizational Design |
Scott Stern |
NBER Working Paper No. 6600
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February 1998
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