Alexandre Belloni

About

Alexandre Belloni is the Westgate Distinguished Professor of Decision Sciences and Statistical Science at Duke University and an Amazon Scholar WW FBA. His research spans artificial intelligence, statistics, economics, and operations research. His work focuses on developing and applying advanced methods in causal inference, high-dimensional statistics, econometrics, machine learning, and mechanism design to solve complex decision-making problems in business, public policy, and digital settings. Professor Belloni’s research interests include machine learning and statistics, mechanism design such as contracts and auctions, and optimization, along with their applications. His work has been published in top journals across Economics, Operations Research, and Statistics. He has received several awards, including the 2007 Young Researchers Competition in Continuous Optimization Award, the 2022 Da Vinci award at Amazon SCOT as part of a team, and the 2022 Bank of America Award at Fuqua. He has served on editorial boards in Economics, Statistics, and Operations Research, and was the inaugural Area Editor for the Machine Learning and Data Science area of Operations Research. In addition to his research, Professor Belloni has taught core statistics and data analytics courses to various programs, including the Daytime MBA, Weekend MBA, and Master of Quantitative Management. His academic and professional contributions emphasize the development of advanced analytical methods and their…

Research topics

• Computer Science
• Statistics
• Mathematical optimization
• Mathematics
• Econometrics
• Artificial Intelligence
• Applied mathematics
• Telecommunications

Selected publications

• Conditional quantile processes based on series or many regressors

  Journal of Econometrics · 2019-05-10 · 84 citations

  articleOpen access1st author
  PublisherOA PDFDOI

• Uniformly valid post-regularization confidence regions for many functional parameters in z-estimation framework

  The Annals of Statistics · 2018-09-11 · 68 citations

  articleOpen access1st author

  In this paper, we develop procedures to construct simultaneous confidence bands for ${\tilde{p}}$ potentially infinite-dimensional parameters after model selection for general moment condition models where ${\tilde{p}}$ is potentially much larger than the sample size of available data, $n$. This allows us to cover settings with functional response data where each of the ${\tilde{p}}$ parameters is a function. The procedure is based on the construction of score functions that satisfy Neyman ortho…

  PublisherOA PDFDOI

• Simultaneous confidence intervals for high-dimensional linear models with many endogenous variables

  2018-08-15 · 26 citations

  preprint1st authorCorresponding

  High-dimensional linear models with endogenous variables play an increasingly important role in recent econometric literature. In this work we allow for models with many endogenous variables and many instrument variables to achieve identification. Because of the high-dimensionality in the second stage, constructing honest confidence regions with asymptotically correct coverage is non-trivial. Our main contribution is to propose estimators and confidence regions that would achieve that. The appro…

  PublisherDOI

• Subvector Inference in Partially Identified Models with Many Moment\n Inequalities

  arXiv (Cornell University) · 2018-06-29 · 13 citations

  preprintOpen access1st authorCorresponding

  This paper considers inference for a function of a parameter vector in a\npartially identified model with many moment inequalities. This framework allows\nthe number of moment conditions to grow with the sample size, possibly at\nexponential rates. Our main motivating application is subvector inference,\ni.e., inference on a single component of the partially identified parameter\nvector associated with a treatment effect or a policy variable of interest.\n Our inference method compares a MinMax…

  PublisherOA PDFDOI

• Subvector Inference in Partially Identified Models with Many Moment Inequalities

  arXiv (Cornell University) · 2018-06-29 · 10 citations

  preprintOpen access1st authorCorresponding

  This paper considers inference for a function of a parameter vector in a partially identified model with many moment inequalities. This framework allows the number of moment conditions to grow with the sample size, possibly at exponential rates. Our main motivating application is subvector inference, i.e., inference on a single component of the partially identified parameter vector associated with a treatment effect or a policy variable of interest. Our inference method compares a MinMax test st…

  PublisherOA PDFDOI

Frequent coauthors

• Victor Chernozhukov

  150 shared

• Christian Hansen

  University of Chicago

  45 shared

• Denis Chetverikov

  23 shared

• Iván Fernández‐Val

  22 shared

• Kengo Kato

  Cornell University

  18 shared

• Ying Wei

  17 shared

• Mingli Chen

  16 shared

• Robert M. Freund

  Massachusetts Institute of Technology

  15 shared

Awards & honors

• 2007 Young Researchers Competition in Continuous Optimizatio…
• 2022 Da Vinci award at Amazon SCOT
• 2022 Bank of America Award at Fuqua

Similar researchers at Duke University

• Dan Arielyh-97
• James Bettmanh-70
• Ravi Bansalh-67