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 application to real-world decision-making challenges.

Research topics

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

Selected publications

• Approximately Optimal Mechanism for Multiunit Demand Buyers with Post-Allocation Inspection

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Approximately Optimal Online Mechanism for Multiunit Demand Buyers with Post-Allocation Inspection

  2025-07-02

  articleOpen access1st authorCorresponding

  We consider a mechanism design problem where an auctioneer allocates k units of an item to multiunit demand buyers arriving in an online fashion, each with a constant private marginal value. The auctioneer can inspect buyers allocated to learn their types and use this information to lower payments to reward truthful buyers. By using tools from the Calculus of Variations, we fully characterize the optimal mechanism for a single-buyer problem subject to an upper bound on the allocation expectation. In particular, we characterize the optimal allocation strategy as the solution of an ordinary differential equation and establish that it is a continuous and monotonically increasing function of the buyer's types. With the solution to the single-buyer problem, and by using connections to the prophet inequality literature, we design an online mechanism that achieves [EQUATION] of the optimal (offline) revenue, where αmax denotes the highest fraction of all units a buyer requests.1

  PublisherOA PDFDOI

• DESENVOLVIMENTO DE UM SISTEMA DE GESTÃO INTEGRADA ENTRE DADOS DE UM ERP E DASHBOARDS UTILIZANDO POWER BI

  Revista Campo da História · 2025-02-12

  articleOpen access1st authorCorresponding

  Este trabalho apresenta o desenvolvimento de um sistema de gestão integrada, conectando um ERP a dashboards interativos utilizando o Power BI e o Firebase. A importância da integração de ferramentas de Business Intelligence (BI) para a visualização de dados empresariais tem sido crucial para a tomada de decisões estratégicas em tempo real, especialmente em ambientes corporativos. Neste projeto, os dados foram obtidos diretamente da plataforma data.world, uma solução eficiente para a integração de dados com o Power BI. O sistema visa facilitar a visualização de dados estratégicos para diferentes setores da organização, como vendas e recursos humanos. O método envolveu a utilização do Firebase para autenticação de usuários e gestão de permissões, e a integração com o Power BI para visualização em tempo real de relatórios gerados. Os principais resultados incluem uma melhoria significativa na acessibilidade e organização das informações, além de um sistema flexível que atende a diferentes níveis hierárquicos da empresa.

  PublisherOA PDFDOI

• Ballot Design and Electoral Outcomes: The Role of Candidate Order and Party Affiliation

  ArXiv.org · 2025-07-22

  preprintOpen access

  We use causal inference to study how designing ballots with and without party designations impacts electoral outcomes when partisan voters rely on party-order cues to infer candidate affiliation in races without designations. If the party orders of candidates in races with and without party designations differ, these voters might cast their votes incorrectly. We identify a quasi-randomized natural experiment with contest-level treatment assignment pertaining to North Carolina judicial elections and use double machine learning to accurately capture the magnitude of such incorrectly cast votes. Using precinct-level election and demographic data, we estimate that 11.8% (95% confidence interval: [4.0%, 19.6%]) of democratic partisan voters and 15.4% (95% confidence interval: [7.8%, 23.1%]) of republican partisan voters cast their votes incorrectly due to the difference in party orders. Our results indicate that ballots mixing contests with and without party designations mislead many voters, leading to outcomes that do not reflect true voter preferences. To accurately capture voter intent, such ballot designs should be avoided.

  PublisherOA PDFDOI

• Quantile graphical models: Prediction and conditional independence with applications to systemic risk

  Journal of Econometrics · 2025-10-09 · 4 citations

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Optimal Auction Design with Deferred Inspection and Reward

  Operations Research · 2024-03-28 · 5 citations

  article

  In a variety of auction contexts, particularly those facilitated by digital marketplaces, auctioneers are now empowered to review the valuations submitted by purchasers and modify the final payments accordingly. Alaei et al. formulate and study the optimal design of such auctions, demonstrating that both the analysis and implementation vastly depart from classical settings. In particular, the analysis requires tools from convex analysis and the calculus of variations, which could be of independent interest in other mechanism design questions.

  PublisherDOI

• Anti-Concentration Inequalities for the Difference of Maxima of Gaussian Random Vectors

  arXiv (Cornell University) · 2024-08-23

  preprintOpen access1st authorCorresponding

  We derive novel anti-concentration bounds for the difference between the maximal values of two Gaussian random vectors across various settings. Our bounds are dimension-free, scaling with the dimension of the Gaussian vectors only through the smaller expected maximum of the Gaussian subvectors. In addition, our bounds hold under the degenerate covariance structures, which previous results do not cover. In addition, we show that our conditions are sharp under the homogeneous component-wise variance setting, while we only impose some mild assumptions on the covariance structures under the heterogeneous variance setting. We apply the new anti-concentration bounds to derive the central limit theorem for the maximizers of discrete empirical processes. Finally, we back up our theoretical findings with comprehensive numerical studies.

  PublisherOA PDFDOI

• Adversarial Estimation of Assortment Probabilities under Independence Structure

  arXiv (Cornell University) · 2024-10-19

  preprintOpen access1st authorCorresponding

  We consider the problem of estimating assortment probabilities, which is common in operations management applications, including product bundling, advertising, etc. Existing approaches typically model each assortment as a category and apply multinomial models to estimate the choice probabilities; while computationally convenient, these methods do not exploit independence structures in the joint distribution and may therefore be statistically inefficient when the total number of items is large. Using the representation from Bahadur (1959), we relate the sparsity of the generalized correlation coefficients to the independence structure of the binary components. We formulate the problem as estimating a high-dimensional vector of generalized correlation coefficients, together with low or moderate-dimensional nuisance parameters corresponding to the marginal probabilities. We develop a regularized adversarial estimator that attains the optimal rate under standard regularity conditions while remaining computationally feasible. The framework naturally extends to settings with covariates. We apply the proposed estimators to causal inference with multiple binary treatments and show substantial finite-sample improvements over non-adaptive methods. Numerical studies corroborate the theoretical results.

  PublisherOA PDFDOI

• High-dimensional latent panel quantile regression with an application to asset pricing

  The Annals of Statistics · 2023-02-01 · 9 citations

  preprintOpen access1st authorCorresponding

  We propose a generalization of the linear panel quantile regression model to accommodate both sparse and dense parts: sparse means that while the number of covariates available is large, potentially only a much smaller number of them have a nonzero impact on each conditional quantile of the response variable; while the dense part is represent by a low-rank matrix that can be approximated by latent factors and their loadings. Such a structure poses problems for traditional sparse estimators, such as the ℓ1-penalized quantile regression, and for traditional latent factor estimators such as PCA. We propose a new estimation procedure, based on the ADMM algorithm, that consists of combining the quantile loss function with ℓ1 and nuclear norm regularization. We show, under general conditions, that our estimator can consistently estimate both the nonzero coefficients of the covariates and the latent low-rank matrix. This is done in a challenging setting that allows for temporal dependence, heavy-tail distributions and the presence of latent factors. Our proposed model has a “Characteristics + Latent Factors” Quantile Asset Pricing Model interpretation: we apply our model and estimator with a large-dimensional panel of financial data and find that (i) characteristics have sparser predictive power once latent factors were controlled and (ii) the factors and coefficients at upper and lower quantiles are different from the median.

  PublisherOA PDFDOI

• Latent Agents in Networks: Estimation and Targeting

  Operations Research · 2023-06-22 · 4 citations

  article

  Latent Agents in Networks: Estimation and Targeting In “Latent Agents in Networks: Estimation and Targeting,” Baris Ata, Alexandre Belloni, and Ozan Candogan address the problem of estimating network effects in a setting in which data only on a subset of agents is available. In this setting, the observable agents influence each other’s outcomes both directly and indirectly through their influence on the latent agents. Even in sparse networks, the combination of direct and indirect network effects yields a nonsparse influence structure that makes estimation challenging. The authors overcome this challenge and provide an estimation algorithm that performs well in high-dimensional settings. They also establish convergence rates for their proposed estimator and show that their performance guarantees are valid for a large class of networks. Finally, the authors demonstrate the application of their algorithm to a targeted advertising problem, in which it can be used to obtain asymptotically optimal advertising decisions despite the presence of latent agents.

  PublisherDOI

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