VICTOR CHERNOZHUKOVFord International Professor
Department of Economics + Center for Statistics and Data Science
Massachusetts Institute of Technology
50 Memorial Drive,E52-524;
Cambridge, MA 02142, USA;
E-mail: vchern at mit.edu.
Web: victorchernozhukov.com
Book: causalml-book.org
Scholar Pro le: V. Chernozhukov

EDUCATION
Stanford University, Ph.D. Economics, 2000. Dissertation: Conditional Extremes and NearExtremes: Concepts, Inference, and Economic Applications. Committee: T. Amemiya, P. Bajari, T.
MaCurdy.
University of Illinois at Urbana-Champaign, M.S. Statistics, 1997
CURRENT ACADEMIC POSITIONS
Massachusetts Institute of Technology, Department of Economics & Center for Statistics andData Science, Professor, 2008-present;
University College London, Honorary Professor and CEMMAP Fellow; since 2023.
PREVIOUS ACADEMIC POSITIONSUniversity of Chicago, Department of Economics, Visiting Associate Professor, Spring 2007
Massachusetts Institute of Technology, Department of Economics, Associate Professor (withtenure), 2005-2008
Massachusetts Institute of Technology, Department of Economics, Assistant Professor,
2000-2005

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ACADEMIC SERVICE
Co-author of the Dual Ph.D. degree in Statistics + X at MIT — new Interdisciplinary
Ph.D. program by the MIT’s Institute for Data, Systems, and Society and Departments of
Economics, Mathematics, Political Science, and others;
Co-author of the new B.S. degree 6-14 in Computer Science, Economics, and Data
Science at MIT;
Co-author of the Minor in Statistics for B.S. degree candidates at MIT.
Co-organizer and co-founder of the MIT Stochastics and Statistics Seminar.
Co-adviser to Doctoral and Post-Doctoral Scholars. Christian Hansen, Allen Ferrell,
Ivan Fernandez-Val, Alfred Galichon, Alexandre Belloni, Konrad Menzel, Igor Makarov,
Mathew Harding, Oleg Rytchkov, Moshe Cohen, Paul Schrimpf, Arun Chandraksekhar,
Denis Chetverikov, Blaise Melly, Kengo Kato, Kaspar Wuthrich, Tetsuya Kaji, Vira
Semenova, Yaroslav Mukhin, Mert Demirer, + current students.
Co-founder of the Structural Reading Group at MIT Economics. A reading group for graduate
students at MIT Economics focusing on structural methods and structural equation modelling.
Co-Author of the Online Professional Education Class at the MIT xPro platform: “DataScience: Data to Insights,” 2015-present
Instructor in Summer and Short Courses on ``Machine Learning for Causal and
Structural Inference”, NBER, St. Gallen, Ljubljana University, The Swiss Bank Study Center atGerzensee , Georgetown University, CEMMAP (for many years).
Inaugural Moderator of the Economics (Econometrics) ArXiv (launch September 2017).
Co-Editor for Econometrics Journal; Economic Theory; Econometric Theory (past).

Co-Author of a new editorial policy for Econometrics Journal, meant to have papers
focusing on key ideas, while dramatically shortening the submission-to-publication times.

INDUSTRY SERVICE
Amazon. Com (Core Arti cial Intelligence); Senior Principal Scientist; 2018-2020; Amazon
Scholar since 2020
Amazon. Com (Central Economics); Independent Consultant on Data Analytics, 2015-2018
The State Street Corporation (Operational Risk Division); Independent Consultant,
2009-2012;
AWARDS and HONORS
Inaugural Sir David Cox Lecture, Institute of Mathematical Statistics and Bernoulli World
Congress, 2024.
Fisher-Shultz Lecture, Econometric Society, 2019.
Bessel Award, Humboldt Foundation, Awarded in 2018.
Fellow of the Institute of Mathematical Statistics, Elected in 2019 “for pathbreaking
contributions to high dimensional statistics”.
American Academy of Arts and Sciences, Fellow, Elected in 2016.
E.J. Hannan Lecture, The Australasian Econometric Society Meeting, 2016
Best Graduate Teacher, MIT Economics Department, Elected by Economics Graduate Student
Association, 2015.
Inaugural Cowles Foundation Lecture, North American Econometric Society Meeting, 2009
Fellow of the Econometric Society, Elected in 2009
International Fellow, University College London, CEMMAP, 2009-present
Alfred P. Sloan Research Fellowship, 2005-2007
Castle-Krob Career Development Chair, 2004-2007
Arnold Zellner Award, 2005;
Selection Committee: D. Andrews, B. Hansen, G. Koop, and A. Lewbel.
Alfred P. Sloan Doctoral Dissertation Fellowship, 1999-2000
American Collegiate Consortium Scholarship, 1993-1994
MAJOR GRANTS
National Science Foundation, 2002-2018
RESEARCH

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Moment Inequalities, Partial Identi cation, Set Estimation. Set identi cation analysis,
estimation, and inference in partially identi ed models, especially moment inequality
models. Empirical application: inference on Hansen-Jaganathan sets in nance; reexamining
racial and gender wage gaps using bounds analysis.

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III.

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Causal Inference with High-Dimensional Data Using Machine Learning and Arti cialIntelligence. Modeling, estimation, and inference with high-dimensional data in economics.
Focus on program evaluation and causal inference with high-dimensional data using modern
statistical methods, a.k.a. machine learning. Empirical Applications: demand analysis withmany prices; hedonic price models using AI; the e ects of guns on homicide rates;

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II.

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Covid-19 Pandemic Research. Impact of mask mandates and business closure policies in an
early pandemic. The e ect of school reopening on the spread of Covid-19. Optimal targeted“lock-down” policies in a multi-group SIR framework.

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IV.

Quasi-Bayesian Estimation. A computationally attractive alternative to the extremum
estimation in structural econometric models. Computational complexity analysis. Sandwich
formulas to correct Bayesian inference.

V.

Shape Restrictions in Econometric Models. Exploiting shape restrictions to improve
estimation and inference on structural functions, including conditional and structural
quantile functions, growth curves, and Edgeworth and Cornish-Fisher expansions.

VI.

Extremes and Nonstandard Models. Model and inference for extreme and near-extreme
conditional quantiles. Applications to market and birthweight risks. Estimation and inference
in models of equilibrium search, standard auction models, and production frontiers.

BOOKS
Handbook of Quantile Regression; with R. Koenker et. al.; 2017; CRC Press.
Applied Causal Inference Powered by ML and AI; with. C. Hansen, N. Kallus, M. Spindler and V.
Syrgakanis; causalml-book.org
Adventures in Introductory Econometrics, with D. Chetverikov, I. Fernandez-Val, W. Newey; dra

PAPERS (papers dated a er 2007 are available via ARXIV.ORG)

1. “Conditional Value-at-Risk: Aspects of Modeling and Estimation,” with L. Umantsev, Empirical
Economics, 26, pp. 271-293, 2001.
2. “Three-step Censored Quantile Regression and Extramarital A airs,” with H. Hong, Journal of theAmerican Statistical Association, 2002.
3. “An MCMC Approach to Classical Estimation,” with H. Hong, Journal of Econometrics, 2003.
Awarded the 2005 Biannual Arnold Zellner Award.
Selection Committee: D. Andrews, B. Hansen, G. Koop, and A. Lewbel.
4. “Likelihood Inference in a Class of Non-Regular Econometric Models,” with H. Hong,
Econometrica, vol.72 (2), pp. 1445-1480, 2004.
5. “The Impact of 401(k) Participation on the Wealth Distribution: An Instrumental Quantile
Regression Analysis,” with C. Hansen, The Review of Economics and Statistics, 2004.
6. “An Instrumental Variable Model of Quantile Treatment E ects,” with C. Hansen, Econometrica,
2005.
7.

“Extremal Quantile Regression,” The Annals of Statistics, 2005.

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8. “Subsampling Inference on Quantile Regression Processes (with an Application to a Reemployment Experiment)”, with I. Fernandez-Val, Sankhya, 2005.

9. “Inference on Instrumental Quantile Processes for Structural and Treatment E ect Models,” with C.
Hansen, Journal of Econometrics, 2006.
10. “Quantile Regression under Misspeci cation and the U. S. Wage Structure,” with J. Angrist and I.
Fernandez-Val, Econometrica, 2006.
11. “Estimation and Inference on Parameter Sets in Econometric Models,” with H. Hong and E. Tamer,
Econometrica, 2007.
12. “Extremal Quantiles and Value-at-Risk”, with Songzi Du, 2007, Palgrave Dictionary of Economics.
13. “Instrumental Variable Identi cation and Estimation of Non-separable Models,” 2007, with G.
Imbens and W. Newey, Journal of Econometrics.
14. “The Reduced Form: A Simple Approach to Inference with Weak Instruments”, with C. Hansen,
Economics Letters, 2007.
15. “Instrumental Quantile Regression: A Robust Inference Approach,” C. Hansen, Journal of
Econometrics, 2007.
16. “Finite-Sample Inference in Quantile Regression Models,” with C. Hansen and M. Jansson, Journalof Econometrics, 2007.
17. “Admissible Tests for Instrumental Regression,” with C. Hansen and M. Jansson, Econometric
Theory, 2008.
18. “Computational Complexity of MCMC-Based Estimators in Large Samples,” with A. Belloni, The
Annals of Statistics, 2009.
19. “Improving Point and Interval Estimators of Monotonic Functions by Rearrangement,” with I.
Fernandez-Val and A. Galichon, Biometrika, 2009.
20. “Quantile and Probability Curves without Crossing,” with I. Fernandez-Val and A. Galichon,
Econometrica, 2010.
21. “Rearranging Edgeworth-Cornish-Fisher Expansions,” with I. Fernandez-Val and A. Galichon,
Economic Theory, 2010.
22. “Sensitivity and Set-Identi cation Analysis of the Regression Model with Tobin Regressors”, with T.
Stocker and R. Rigobon, Quantitative Economics, 2010.
23. “Inference for Extremal Quantile Regression Models, with an Application to Market andBirthweight Risks,” with I. Fernandez-Val, The Review of Economic Studies, 2011
24. “L1-Penalized Quantile Regression in High-Dimensional Sparse Models,” with A. Belloni, The
Annals of Statistics, 2011.
25. “High-Dimensional Sparse Econometric Models: An Introduction,” with A. Belloni, Springer
Lecture Notes, 2011. (Refereed)

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26. “Square Root Lasso: Pivotal Recovery of Sparse Functions via Conic Programming”, with A. Belloni
and L. Wang, Biometrika, 2011.

27. “Sparse Models and Methods for Instrumental Regression with Application to Eminent Domain”,
with A. Belloni, C. Hansen, D. Chen, Econometrica, 2012.
28. “Intersection Bounds: Estimation and Inference,” with S. Lee and A. Rosen, Econometrica, 2013.
29. “Average and Quantile E ects in Nonlinear Panel Data Models,” with J. Hahn, I. Fernandez-Val, W.
Newey, Econometrica, 2013.
30. “Least Squares a er Model Selection in High-Dimensional Linear Regression Model”, with A.
Belloni, Bernoulli, 2013. (Arxiv 2009).
31. “Inference Methods for High-Dimensional Sparse Econometric Models,” with A. Belloni and C.
Hansen, World Congress of Econometric Society 2010, Advances in Economics andEconometrics, 2011.
Invited lecture presented at the World Congress of Econometric Society in 2010, withdiscussion by S. Bonhomme.
32. “Quantile Models with Endogeneity”, with C. Hansen, invited article, Annual Review of
Economics, (5) 2013.
33. “Inference on Counterfactual Distributions,” I. Fernandez-Val and B. Melly, Econometrica, 2013.
34. “Gaussian Approximations and Gaussian Multiplier Bootstrap for Maxima of Sums of HighDimensional Random Vectors”, with D. Chetverikov and K. Kato, Annals of Statistics, 201335. "Identi cation in Semiparametric and Nonparametric Conditional Moment Models", with X. Chen,
S. Lee, and W. Newey, Econometrica, 2014.
36. “Comparison and Anti-Concentration Bounds for Maxima of Gaussian Vectors”, with D.
Chetverikov and K. Kato. Probability Theory and Related Fields, 2015 (ArXiv 2013)
37. “Inference on Treatment E ects with High-Dimensional Controls, with Application to Abortion
and Crime”, with A. Belloni and C. Hansen. The Review of Economic Studies, 2014 (ArXiv 2011)
38. “Posterior Inference in Curved Exponential Families under Increasing Dimension”, with A. Belloni,
Econometrics Journal, 2014.
39. “Pivotal Estimation via Square-Root Lasso in Non-parametric Regression”, with A. Belloni and L.
Wang. Annals of Statistics, 2014. (Arxiv 2011)
40. “Inference on Structural and Treatment E ects with High-Dimensional Data”, with A. Belloni and C.
Hansen. Journal of Economic Perspectives, 2014.
41. “Gaussian Approximation of Suprema of Empirical Processes”, with D. Chetverikov and K. Kato.
Annals of Statistics, 2014.
42. “Anti-Concentration and Con dence Bands in Nonparametric Problems”, with D. Chetverikov andK. Kato. Annals of Statistics, 2014.

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43. “Inference on Sets in Finance,” with E. Kokatulum and K. Menzel, Quantitative Economics, 2015.

44. “Quantile Regression under Censoring and Endogeneity,’’ with I. Fernandez-Val and A. Kowalski.
Journal of Econometrics, 2015.
45. “Uniform Post-Selection Inference in LAD regression and other Z-estimation Problems”, with A.
Belloni and K. Kato, Biometrika, 2014.
46. “Fragility of Asymptotic Agreement under Bayesian Learning,” with D. Acemoglu and M. Yildiz.
Theoretical Economics, 2015.
47. “Some New Asymptotic Theory for Least Squares Series Estimators”, with A. Belloni, D.
Chetverikov, and K. Kato. Journal of Econometrics, 2015.
48. “Nonparametric Identi cation in Panels using Quantiles”, with I. Fernandez-Val, W. Newey et. al.,Journal of Econometrics, 2015.
49. “Implementing Intersection Bounds in Stata”, with W. Kim, S. Lee, A. Rosen, Stata Journal, 2015.
50. “Post-Selection and Post-Regularization Inference in Large Linear Models with Many Controls andInstruments”, with C. Hansen and M. Spindler, American Economic Review, Papers andProceeding, 2015
51. “Post-Selection and Post-Regularization Inference: An Elementary, General Approach”, with C.
Hansen and M. Spindler, Annual Review of Economics, 2015.
52. “Censored Quantile Instrumental Variable Estimation with Stata”, with I. Fernandez-Val, SukjinHan, Amanda Kowalski; Revision Requested, Stata Journal; ArXiv 2016
53. “Honest Con dence Regions for High-Dimensional Sparse Generalized Linear Models”, Journal ofBusiness and Economic Statistics, 2016, with A. Belloni and W. Ying
54. “Inference on Treatment E ects with High-Dimensional Panel Data, with an Application to Gun
Control”, Journal of Business and Economic Statistics, 2016, with D. Kozbur, and C. Hansen
55. “Empirical and Gaussian Bootstraps for Suprema of Empirical Processes of Increasing Complexity,
and Related Gaussian Couplings”, with D. Chetverikov and K. Kato, Stochastic Processes andTheir Applications, 2016. Memorial Issue in honor of Evarist Gine.
56. “Vector Quantile Regression”, with G. Carlier and A. Galichon, Annals of Statistics, 2016
57. “Program Evaluation with High-Dimensional Data”, with A. Belloni, C. Hansen, I. Fernandez-Val,
Econometrica, 2017
58. “A Lava Attack on the Recovery of Sums of Sparse and Dense Signals”, with C. Hansen and Y. Liao,
Annals of Statistics, 2017
59. “Monge-Kantorovich Depth, Quantiles, and Ranks”, with A. Galichon, M. Hallin, M. Henry, 2014,
Annals of Statistics, 2017

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60. “Central Limit Theorems and Bootstrap in High Dimensions”, with D. Chetverikov and K. Kato,
2015, Annals of Probability, 2017

61. “Double/Debiased/Neyman Machine Learning for Treatment E ects”, with Denis Chetverikov,
Mert Demirer, Esther Du o, Christian Hansen, and Whitney Newey, American Economic
Review, Papers & Proceedings, 2017
62. “Double/De-Biased Machine Learning for Treatment and Causal Parameters”, with Denis
Chetverikov, Mert Demirer, Esther Du o, Christian Hansen, Whitney Newey, and James Robins,
Econometrics Journal, 2018
63. “HDM: High-Dimensional Metrics”, an R-package at r-project.org, accompanied by a refereed
paper published in R Journal, 2017, with C. Hansen, M. Spindler
64. “Quantreg.nonpar: An R Package for Performing Nonparametric Series Quantile Regression”, with
M. Lipsitz, I. Fernandez-Val, A. Belloni; an R-package at r-project.org, accompanied by a refereed
paper published in R Journal, 2017;
65. “Counterfactual: An R Package for Counterfactual Analysis”, with Mingli Chen, Iván FernándezVal, Blaise Melly; an R-package at r-project.org, accompanied by a refereed paper published in R
Journal, 2018.
66. “The Sorted E ects Method: Discovering Heterogeneous Partial E ects Beyond Their Averages”,
with I. Fernandez-Val and Y. Luo, Econometrica, 2018.
67. “Inference on causal and structural parameters using many moment inequalities”, with D.
Chetverikov and K. Kato, Review of Economic Studies, 2019
68. “Robust Inference in Approximately Sparse Quantile Regression Models”, with A. Belloni and K.
Kato, Journal of the American Statistical Association, 2019.
69. “Uniformly Valid Post-Regularization Con dence Regions for Many Functional Parameters in ZEstimation Framework”, with A. Alexandre Belloni, Victor Chernozhukov, Denis Chetverikov, Ying
Wei; Annals of Statistics, 2018;
70. “Vector quantile regression beyond correct speci cation,” with G. Carlier and A. Galichon, Journalof Multivariate Analysis. 2018
71. “Conditional Quantile Processes based on Series or Many Regressors (with an Application to
Gasoline Demand),” with A. Belloni & D. Chetverikov & I. Fernandez-Val, ArXiv 2013. Forthcoming,
J. Econometrics
72. “Generic Inference on Quantile and Quantile E ect Functions for Discrete Outcomes”, with Iván
Fernández-Val, Blaise Melly, Kaspar Wüthrich. Conditionally Accepted, Journal of the American
Statistical Association, 201873. ``Nonseparable Multinomial Choice Models in Cross-Section and Panel Data”; with I. FernandezVal and W. Newey; ArXiv, Journal of Econometrics, 2021;
74. ``On Cross-Validated Lasso”, with D. Chetverikov and Z. Liao; ArXiv; Annals of Statistics, 2021
75. “Identi cation of Hedonic Equilibrium and Nonseparable Simultaneous Equations”, with A.
Galichon M. Henry, B. Pass, ArXiv 2015; Journal of Political Economy

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76. “Shape-Enforcing Operators for Point and Intervals Estimators”, with Xi Chen, I. Fernandez-Val,
Scott Kostyshak, and Y. Luo; ArXiv 2018. Journal of Machine Learning Research, 2021.

77. ``Network and Panel Quantile E ects via Distribution Regression”, with I. Fernandez-Val and M.
Weidner; ArXiv 2018; Journal of Econometrics, 2021.
78. ``Distribution Regression with Sample Selection, with an Application to the U.K. Wage Structure,”
with I. Fernandez-Val and Siyi Luo; ArXiv 2018; Journal of Political Economy, forthcoming.
79. ``Debiased machine learning of conditional average treatment e ects and other causal functions;with V. Semenova; ArXiv 2018; Econometrics Journal, 2021
80. ``Generic Machine Learning Inference on Heterogenous Treatment E ects in Randomized
Experiments”, with E. Du o, M. Demirer; I. Fernandez-Val; ArXiv 2017; Econometrica,
forthcoming.
81. ``Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls”, with
Yinchu Zhu and Kaspar Wuthrich; ArXiv 2017, Journal of the American Statistical Association,
2021
82. ``Inference on Heterogeneous Treatment E ects in High-Dimensional Panel Data Models with
Weak Dependence,” with V. Semenova, M. Goldman, M. Taddy; ArXiv 2017; Quantitative
Economics, 2013.
83. ``Semi-Parametric Estimation of Structural Functions in Nonseparable Triangular Models”; with
with I. Fernandez-Val, W. Newey, S. Stouli, Francis Vella; ArXiv 2017; Journal of Econometrics,
2020
84. ``Detailed Proof of Nazarov’s Inequality”, with K. Kato and D. Chetverikov; 2017; ArXiv only.
85. ``The Impact of Big Data on Firm Performance: An Empirical Investigation”, with P. Bajari, A.
Hortacsu, J. Suzuki, American Economic Association, Papers & Proceedings, 2019 (long version:
NBER).
86. ``Exact and Robust Conformal Inference Methods for Predictive Machine Learning with
Dependent Data”, with K. Wuthrich and Y. Zhu; Computational Learning Theory (COLT), 2018.
87. ``Double/Debiased Machine Learning of Global and Local Functionals with Regularized Riesz
Representers”, with W. Newey and J. Robins; ArXiv 2018. Econometrics Journal; 2022
88. ``Automatic Debiased Machine Learning of Causal and Structural E ects,” with W. Newey and R.
Singh; ArXiv 2018; Econometrica; 2022
89. ``LASSO-Driven Inference in Time and Space”, with W. Hardle, C. Huang, W. Wang; ArXiv 2018,
Annals of Statistics, 2021.
90. ``Optimal Targeted Lockdowns in a Multi-Group SIR Models”, with D. Acemoglu, M. Whinston,
Ivan Werning’, American Economic Review: Insights, 2021.
91. ``Vector Quantile Regression and Optimal Transport, from Theory to Numerics”, with G. Carlier, G.
De Bee, A. Galichon, Empirical Economics, 2021

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92. ``The Association of Opening K-12 Schools and Colleges with the Spread of Covid-19 in the UnitedStates: County-Level Panel Data Analysis”, with H. Kasahara, P. Schrimpf. ArXiv 2021; Proceedingof the National Academy of Sciences, 2021.

93. ``Nearly Optimal Central Limit Theorem and Bootstrap in High Dimensions”, with D. Chetverikov
and Yuta Koike. ArXiv 2020; Annals of Applied Probability, 2023.
94. ``Improved Central Limit Theorem and bootstrap approximations in high dimensions”, with D.
Chetverikov, K. Kato, and Y. Koike, ArXiv; Annals of Statistics, October 2022.
95. ``Distributional Conformal Regression”, with K. Wuthrich and Y. Zhu, ArXiv Proceedings of the
National Academy of Sciences, 2021
96. ``Fast Algorithms for the Quantile Regression Process”, with I. Fernandez Val and B. Melly,
Empirical Economics, 2021
97. ``Sorted E ects: Sorted Causal E ects in R”, with S. Chen, I. Fernandez-Val, and Y. Luo, R Journal,
2020
98. ``Mastering Panel Metrics: Causal Impact of Democracy on Growth”, ArXiv, 2019, AEA Papers andProceedings, 2020, with S. Chen and I. Fernandez-Val;99. ``A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees”;
with W. Newey and R. Singh; ArXiv 2021; Biometrika, 2023; (published online June 2022).
100. ``RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets andRandom Forests”, with W. Newey, V. Syrkganis, V. Quintas-Martinizez; ArXiv 2021; International
Conference on Machine Learning, 2022
101. ``Deeply-Debiased O -Policy Interval Estimation”; with Chengchun Shi, Runzhe Wan, Rui Song;
ArXiv 2021; International Conference on Machine Learning, 2021
102.``Hedonic Prices and Quality-Adjusted Price Indices Powered by AI”, with P. Bajari, Z. Cen, M.
Manukonda, R. Huerta, et al. CEMMAP working paper, 2021
103. ``DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python”, with
P. Bach, M. Kurz, M. Spindler; ArXiv 2021; Journal of Machine Learning Research, 2022.
104.``High-dimensional Data Bootstrap”, with Denis Chetverikov, Kengo Kato, Yuta Koike, Annual
Review of Statistics, 2023 (to appear).
105.“Constrained Moment Conditions Models”, submitted, with A. Santos and W. Newey,
Econometrica, 2022 (to appear); ArXiv 2016.
106.“Locally Robust Semi-parametric Estimation”, with Juan Carlos Escanciano, Hidehiko Ichimura,
Whitney K. Newey, James Robins; ArXiv; Econometrica; 2022;
107. “Best Linear Approximations to Set-Identi ed Functions (with an Application to Gender Wage
Gap)”, 2009, with A. Chandraksekhar, F. Molinari , and P. Schrimpf; ArXiv Quantitive Economics;
Revise and Resubmit.
108.``High-Dimensional Econometrics and Regularized GMM”, forthcoming, Handbook of
Econometrics v7, under revision, with A. Belloni, D. Chetverikov, C. Hansen, K. Kato; ArXiv 2018;

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109. ``Toward Personalized Inference on Individual Treatment E ects”, with K. Wuthrich and Y. Zhu
Proceedings of the National Academy of Sciences, 2023 (to appear).

110. ``Valid Simultaneous Inference in High-Dimensional Settings” (with the hdm package for R); ArXiv
2019; with P. Bach and M. Spindler; ArXiv 2018111. ``Inference on Weighted Average Value Function in High-Dimensional State Space”, with W.
Newey and V. Semenova, ArXiv 2019.
112. “Quantile Graphical Models: Prediction and Conditional Independence with Applications to
Systemic Risk”, with A. Belloni and M. Chen; ArXiv 2017113. “Con dence Bands for Coe cients in High-Dimensional Linear Models with Errors-in-Variables,”with A. Belloni, A. Kaul; ArXiv 2017;114. ``Minimax Semiparametric Learning With Approximate Sparsity”, with J. Bradic, W. Newey, Y. Zhu
115. ``Subvector Inference in Partially Identi ed Models with Many Moment Inequalities”; with A.
Belloni and F. Bugni; ArXiv 2018116. ``Demand Analysis with Many Prices”, with J. Hausman and W. Newey. (NBER 2019).
117. ``Inference for Heterogeneous E ects using Slope Factor Models”, with C. Hansen, Y. Liao, Y. Zhu;
ArXiv 2018.
118. ``Debiased Machine Learning via Riesz Regression”, with W. Newey, Victor Quitas-Martinez,
Vasilis Syrgkanis; ArXiv; 2021
119. ``A t-test for Synthetic Controls”, with K. Wuthrich and Y. Zhu; ArXiv 2018; JPE R & R
120.``Inference for Low-Rank Models”, with Y. Liao, Y. Zhu, C. Hansen; ArXiv 2021; Annals of
Statistics
121. ``Adversarial Estimation of Riesz Representers”, with W. Newey, Rahul Singh, Vasilis Syrgkanis,
ArXiv, 2021
122. ``Causal Bias Quanti cation for Continuous Treatment”, with G. Detomasso, M. Bruckner, P. Shultz;
ArXiv 2021
123. ``Automatic Debiased Machine Learning for Dynamic Treatment E ects”, with Whitney Newey,
Rahul Singh, Vasilis Syrgkanis; ArXiv 2022
124.``Learning Networks with Focally Sparse Structure“; with Chen Huang, Weining Wang; ArXiv 2021
125. ``Long Story Short: Omitted Variable Biases in Machine Learned Causal Models”, with C. Cinelli, W.
Newey, V. Syrganis. A. Sharma; ArXiv 2021;
126.``DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R”, with P.
Bach, M. Kurz, M. Spindler; ArXiv 2021;

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127. ``Weighted Average Causal E ects with Continuous Treatment and Monotone Instrument”, with I.
Fernandez-Val et al.