Research

See an overview of my research interests

My papers are organized by topic to make them easier to find. There is some duplication/cross-listing. For a chronological list of my articles look at my cv. Topics:

Econometric Theory and Machine Learning

Page Title	Coauthors	Citation	Last Revised
Optimal Experimental Design for Staggered Rollouts	Ruoxuan Xiong, Mohsen Bayati, Guido Imbens	arXiv:1911.03764	November 2019
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations	Guido Imbens, Jonas Metzger, Evan Munro	arXiv:1909.02210	September 2019
Estimating Treatment Effects with Causal Forests: An Application	Stefan Wager	Observational Studies 5, September 2019, 21-35
Sufficient Representations for Categorical Variables	Jonathan Johannemann, Vitor Hadad, Stefan Wager	arXiv:1908.09874	August 2019
Synthetic Difference In Differences	Dmitry Arkhangelsky, David A. Hirshberg, Guido W. Imbens, Stefan Wager	arXiv:1812.09970	January 31, 2019
Counterfactual Inference for Consumer Choice Across Many Product Categories	Rob Donnelly, Francisco R. Ruiz, David Blei	arXiv:1906.02635	June 7, 2019
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
SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements	Francisco Ruiz, David Blei	Annals of Applied Statistics (forthcoming), 2019
Machine Learning Methods Economists Should Know About	Guido Imbens	arXiv:1903.10075	March 24, 2019
Generalized Random Forests	Julie Tibshirani, Stefan Wager	Annals of Statistics, January 2019, Vol. 47, Issue 2; pp. 1148-1178
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.
Offline Multi-Action Policy Learning: Generalization and Optimization	Zhengyuan Zhou, Stefan Wager	arXiv:1810.04778	October 10, 2018
Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption	Guido Imbens	arXiv:1808.05293	August 15, 2018
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	June 16, 2018
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests	Stefan Wager	Journal of the American Statistical Association, 2018, 1-15
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
Estimation Considerations in Contextual Bandits	Maria Dimakopoulou, Guido Imbens	arXiv:1711.07077	March 2, 2018
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
The Impact of Machine Learning on Economics		Forthcoming in book: The Economics of Artificial Intelligence: An Agenda
Structured Embedding Models for Grouped Data	Maja Rudolph, Francisco Ruiz, David Blei	Neural Information Processing Systems, 2017: 250-260
Context Selection for Embedding Models	Liping Liu, Francisco Ruiz, David Blei	Neural Information Processing Systems, 2017: 4819-4828
When Should You Adjust Standard Errors for Clustering?	Alberto Abadie, Guido Imbens, Jeffrey Wooldridge	arXiv:1710.02926	October 9, 2017
Matrix Completion Methods for Causal Panel Data Models	Mohsen Bayati, Nikolay Doudchenko, Guido Imbens, Khashayar Khosravi	arXiv:1710.10251	Slides from NBER Summer Institute, Cambridge, July 27, 2017
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	June 6, 2017
The State of Applied Econometrics: Causality and Policy Evaluation	Guido Imbens	Journal of Economic Perspectives, 31(2), Spring 2017: 3-32	July 2016
Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges	Guido Imbens, Thai Pham, Stafan Wager	American Economic Review, 107(5), May 2017: 278-281
The Econometrics of Randomized Experiments	Guido Imbens	Handbook of Economic Field Experiments, 1 (2017): 73-140.
Exact P-values for Network Interference	Dean Eckles, Guido W. Imbens	Journal of the American Statistical Association. 2017.
Beyond Prediction: Using Big Data for Policy Problems		Science. Feb 03, 2017: Vol. 355, Issue 6324, pp. 483-485	Feb 2017
Efficient Policy Learning	Stefan Wager	arXiv:1702.02896	February 2017
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	June 2016
Recursive Partitioning for Heterogeneous Causal Effects	Guido W. Imbens	PNAS, 2016, 113(27):7353-7360; published ahead of print July 5, 2016
Machine Learning and Causal Inference for Policy Evaluation		KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Pages 5-6
A Measure of Robustness to Misspecification	Guido W. Imbens	The American Economic Review, 105(5), May 2015:476-480
Discrete Choice Models with Multiple Unobserved Choice Characteristics	Guido W. Imbens	International Economic Review, 48 (4), November 2007:1159-1192
Nonparametric Approaches to Auctions	Philip A. Haile	Handbook of Econometrics. 2007, Vol. 6, Issue A, Pages 3847–3965
Identification and Inference in Nonlinear Difference-in-Differences Models	Guido W. Imbens	Econometrica, 74(2), March 2006:431-497	Supplemental Material Download CIC Code 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
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
Identification of Standard Auction Models	Philip A. Haile	Econometrica, 70(6), November 2002:2107-2140	August 2000 September 2001
An Empirical Framework for Testing Theories About Complementarity in Organizational Design	Scott Stern	NBER Working Paper No. 6600	February 1998

Auctions, Market Design, and Online Advertising (Theory, Empirical, and Econometrics)

Page Title	Coauthors	Citation	Last Revised
The Impact of Consumer Multi-homing on Advertising Markets and Media Competition	Emilo Calvano, Joshua S. Gans	Management Science, 64(4), April 2018: 1574-1590
The Digital Privacy Paradox: Small Money, Small Costs, Small Talk	Christian Catalini, Catherine E. Tucker	Working Paper	February 2017
Peaches, Lemons, and Cookies: Designing Auction Markets with Dispersed Information	Moshe Babaioff, Michael D. Grubb, Ittai Abraham	Working Paper	December 2016
Position Paper: Sponsored Search Auctions are Combinatorial	Ian A. Kash, Peter Key, David C. Parkes, Mahyar Salek, Moshe Tennenholtz	Working paper	Coming soon
Consumer Tracking and Efficient Matching in Online Advertising Markets	Emilio Calvano, Joshua Gans	Working Paper	Coming soon
Information, Privacy and the Internet - An Economic Perspective		CPB Netherlands Bureau for Economic Policy Analysis	June 2014
Set-Asides and Subsidies in Auctions	Dominic Coey, Jonathan Levin	American Economic Journals: Microeconomics, 5(1): 1-27	February 2013 Appendix
The Impact of Aggregators on Internet News Consumption	Markus Mobius, Jeno Pal	Working Paper	January 2017, February 2012 Version
A Structural Model of Sponsored Search Advertising Auctions	Denis Nekipelov	Working Paper	January 2012
Position Auctions with Consumer Search	Glenn Ellison	Quarterly Journal of Economics, vol. 126(3), 2011, 1213-1270	2010 2008 Working Paper
Comparing Open and Sealed Bid Auctions: Theory and Evidence from Timber Auctions	Jonathan Levin, Enrique Seira	Quarterly Journal of Economics, vol. 126(1), 2011, 207-257	Supplementary Material
The Impact of Targeting Technology on Advertising Markets and Media Competition	Joshua Gans	American Economic Review Papers and Proceedings, May 2010
Skewed Bidding in Pay Per Action Auctions for Online Advertising	Nikhil Agarwal, David C. Yang	American Economic Review, 99(2): 441-47
Nonparametric Approaches to Auctions	Philip A. Haile	Handbook of Econometrics. 2007, Vol. 6, Issue A, Pages 3847–3965
Collusion with Persistent Cost Shocks	Kyle Bagwell	Econometrica,76(3), May 2008:493-540	July 2006 Supplementary Material
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
Collusion and Price Rigidity	Kyle Bagwell, Chris Sanchirico	Review of Economic Studies, 71(2):317-349	1998 version: MIT Working Paper 98-23
Dynamic Auctions with Persistent Private Information	Kyle Bagwell	Working Paper	September 2003
Identification of Standard Auction Models	Philip A. Haile	Econometrica, 70(6), November 2002:2107-2140	August 2000 September 2001
Optimal Collusion with Private Information	Kyle Bagwell	RAND Journal of Economics,32 (3), September 2001: 428-465	MIT Working Paper 99-12
Single Crossing Properties and the Existence of Pure Strategy Equlibria in Games of Incomplete Information		Econometrica, 69 (4) July 2001: 861-890	The July 1997 version has numerical analysis of asymmetric auctions. July 1997 Paper: scpgii.ps. July 1987 Appendix: scpgiiap.ps
Information and Competition in U.S. Forest Service Timber Auctions	Jonathan Levin	Journal of Political Economy, 109 (2), April 2001: 375-417

Empirical Industrial Organization and Econometrics of IO

Page Title	Coauthors	Citation	Last Revised
Counterfactual Inference for Consumer Choice Across Many Product Categories	Rob Donnelly, Francisco R. Ruiz, David Blei	arXiv:1906.02635	June 7, 2019
SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements	Francisco Ruiz, David Blei	Annals of Applied Statistics (forthcoming), 2019
Experienced Segregation	Billy Ferguson, Matthew Gentzkow, Tobias Schmidt	Working Paper	February 2019
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
Beyond Prediction: Using Big Data for Policy Problems		Science. Feb 03, 2017: Vol. 355, Issue 6324, pp. 483-485	Feb 2017
Bitcoin Pricing, Adoption, and Usage: Theory and Evidence	Iva Parashkevov, Vishnu Sarukkai, Jing Xia	Working Paper	August 2016
Set-Asides and Subsidies in Auctions	Dominic Coey, Jonathan Levin	American Economic Journals: Microeconomics, 5(1): 1-27	February 2013 Appendix
The Impact of Aggregators on Internet News Consumption	Markus Mobius, Jeno Pal	Working Paper	January 2017, February 2012 Version
A Structural Model of Sponsored Search Advertising Auctions	Denis Nekipelov	Working Paper	January 2012
Comparing Open and Sealed Bid Auctions: Theory and Evidence from Timber Auctions	Jonathan Levin, Enrique Seira	Quarterly Journal of Economics, vol. 126(1), 2011, 207-257	Supplementary Material
Exchange Rate Fluctuations, Consumer Demand, and Advertising: The Case of Internet Search	Maya Cohen Meidan	Working Paper	November 2010
Discrete Choice Models with Multiple Unobserved Choice Characteristics	Guido W. Imbens	International Economic Review, 48 (4), November 2007:1159-1192
Nonparametric Approaches to Auctions	Philip A. Haile	Handbook of Econometrics. 2007, Vol. 6, Issue A, Pages 3847–3965
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
Identification of Standard Auction Models	Philip A. Haile	Econometrica, 70(6), November 2002:2107-2140	August 2000 September 2001
The Impact of Information Technology on Emergency Health Care Outcomes	Scott Stern	RAND Journal of Economics,33(3), Autumn 2002:399-432
Information and Competition in U.S. Forest Service Timber Auctions	Jonathan Levin	Journal of Political Economy, 109 (2), April 2001: 375-417
Information Technology and Training in Emergency Call Centers	Scott Stern	Proceedings of the Fifty-First Annual Meetings (New York, Jan 3-5, 1999). Madison, WI: Industrial Relations Research Association, pp. 53-60
An Empirical Framework for Testing Theories About Complementarity in Organizational Design	Scott Stern	NBER Working Paper No. 6600	February 1998

Dynamic and Repeated Games and Mechanisms

Page Title	Coauthors	Citation	Last Revised
A Theory of Community Formation and Social Hierarchy	Emilio Calvano, Saumitra Jha	Working Paper	August 2016
Dynamic Auctions with Persistent Private Information	Kyle Bagwell	Working Paper	In progress
Dynamics of Open Source Movements	Glenn Ellison	Journal of Economics & Management Strategy, Vol. 23, No.2, 294-316, Summer 2014	April 2014
An Efficient Dynamic Mechanism	Ilya Segal	Econometrica, Vol. 81, No. 6 (November, 2013), 2463-2485	June 2013
Designing Efficient Mechanisms for Dynamic Bilateral Trading Games	Ilya Segal	American Economic Review Papers and Proceedings, May 2007	January 2007
Collusion with Persistent Cost Shocks	Kyle Bagwell	Econometrica,76(3), May 2008:493-540	July 2006 Supplementary Material
Efficiency in Repeated Trade with Hidden Valuations	David A. Miller	Theoretical Economics, Volume 2, Number 3 (September 2007), 299-354	June 2006
The Optimal Degree of Discretion in Monetary Policy	Andrew Atkeson, Patrick Kehoe	Econometrica, 73 (5), September 2005, 1431-1476
Collusion and Price Rigidity	Kyle Bagwell, Chris Sanchirico	Review of Economic Studies, 71(2):317-349	1998 version: MIT Working Paper 98-23
Optimal Collusion with Private Information	Kyle Bagwell	RAND Journal of Economics,32 (3), September 2001: 428-465	MIT Working Paper 99-12
Investment and Market Dominance	Armin Schmutzler	RAND Journal of Economics, 32 (1), Spring 2001: 1-26

Economics of Organizations

Page Title	Coauthors	Citation	Last Revised
The Allocation of Decision Authority to Human and Artificial Intelligence	Joshua Gans, Kevin A. Bryan	NBER Working Paper no. 26673 DOI: 10.3386/w26673	January 2020
Economists (and Economics) in Tech Companies	Michael Luca	Journal of Economics Perspectives, Vol. 33, no. 1, pp. 209-30, winter 2019
A Theory of Community Formation and Social Hierarchy	Emilio Calvano, Saumitra Jha	Working Paper	August 2016
The Nature and Incidence of Software Piracy: Evidence from Windows	Scott Stern	Chapter in NBER book Economic Analysis of the Digital Economy (2015), Avi Goldfarb, Shane Greenstein, and Catherine Tucker, editors (p.443 - 477)	April 2015
Dynamics of Open Source Movements	Glenn Ellison	Journal of Economics & Management Strategy, Vol. 23, No.2, 294-316, Summer 2014	April 2014
What Does Performance in Graduate School Predict? Graduate Economics Education and Student Outcomes	Larry Katz, Alan B. Krueger, James Poterba, Steve D. Levitt	American Economic Review, 97(2): 512-520	January 2007
The Impact of Information Technology on Emergency Health Care Outcomes	Scott Stern	RAND Journal of Economics,33(3), Autumn 2002:399-432
Organizational Design: Decision Rights and Incentive Contracts	John Roberts	American Economic Review, 91(2), May 2001: 200-205
Mentoring and Diversity	Chris Avery, Peter Zemsky	American Economic Review 90 (4) September 2000: 765-786	October 1998 version (includes labor market model)
Information Technology and Training in Emergency Call Centers	Scott Stern	Proceedings of the Fifty-First Annual Meetings (New York, Jan 3-5, 1999). Madison, WI: Industrial Relations Research Association, pp. 53-60
An Empirical Framework for Testing Theories About Complementarity in Organizational Design	Scott Stern	NBER Working Paper No. 6600	February 1998
Product and Process Flexibility in an Innovative Environment	Armin Schmutzler	RAND Journal of Economics, 26 (4), Winter1995: 557-574

Monotone Comparative Statics and Applications (Value of Information, Uncertainty, Existence of Equilibria, Games with Strategic Substitutes)

Page Title	Coauthors	Citation	Last Revised
The Value of Information in Monotone Decision Problems	Jonathan Levin	Research in Economics (2017), doi: 10.1016/j.rie.2017.01.001
Monotone Comparative Statics Under Uncertainty		Quarterly Journal of Economics,CXVII (1), February 2002, 187-223	1998 Working Paper Version, MIT Working Paper 96-22, with additional results
Single Crossing Properties and the Existence of Pure Strategy Equlibria in Games of Incomplete Information		Econometrica, 69 (4) July 2001: 861-890	The July 1997 version has numerical analysis of asymmetric auctions. July 1997 Paper: scpgii.ps. July 1987 Appendix: scpgiiap.ps
Investment and Market Dominance	Armin Schmutzler	RAND Journal of Economics, 32 (1), Spring 2001: 1-26
Investment and Information Value for a Risk Averse Firm		MIT Working Paper 00-30	November 2000
Characterizing Properties of Stochastic Objective Functions		MIT Working Paper 96-1R	October 2000
Robust Comparative Statics (Draft of Chapters 0-4)	Paul Milgrom, John Roberts		Teaching notes for a graduate micro class integrated with some producer theory Note: this draft may have typos and is generally not polished. I have posted it in response to requests, but please use at your own risk.
Product and Process Flexibility in an Innovative Environment	Armin Schmutzler	RAND Journal of Economics, 26 (4), Winter1995: 557-574

I am grateful to the National Science Foundation, SBR-9631760, SES-9983820, and SES-0351500 for supporting much of the research listed above. Any opinions, findings and conclusions orrecomendationsexpressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF), MIT, Stanford, or the National Bureau of Economic Research.

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