About

Denis Nekipelov is an Associate Professor of Economics and Computer Science (by Courtesy) at the University of Virginia School of Engineering and Applied Science. He holds a Ph.D. in Economics from Duke University, earned in 2008, a Master’s degree in Economics (Cum Laude) from the New Economic School in Moscow, Russia, in 2003, and both a Master of Science and Bachelor of Science in Applied Physics and Mathematics with distinctions from the Moscow Institute of Physics and Technology, also in 2003. His research interests include computational methods of industrial organization and structural economics for big data. His work has contributed to the understanding of treatment effects from combined data, digital economy analysis, preference elicitation for sponsored search advertisers, mechanism design for data science, and properties of Laplace-type estimators. He has received awards such as the ACM EC Best Paper Award in 2015 and holds a US patent related to the analysis of sponsored search auctions.

Research topics

• Computer Science
• Computer Security
• Data Mining
• Mathematical optimization
• Statistics
• Applied mathematics
• Data science
• Econometrics
• Combinatorics
• Mathematics
• Risk analysis (engineering)
• Internet privacy
• Business

Selected publications

• Measuring the Return to Online Advertising: Estimation and Inference of Endogenous Treatment Effects

  Econometrics · 2026-05-12

  preprintOpen access

  In this paper we aim to conduct inference on the “lift” effect generated by an online advertisement display: specifically we want to analyze if the presence of the brand ad among the advertisements on the page increases the overall number of consumer clicks on that page. A distinctive feature of online advertising is that the ad displays are highly targeted—the advertising platform evaluates the (unconditional) probability of each consumer clicking on a given ad, which leads to a higher probability of displaying the ads that have a higher a priori estimated probability of click. As a result, inferring thecausal effect of the ad display on the page clicks by a given consumer from typical observational data is difficult. To address this we propose a multi-step estimator that focuses on the tails of the consumer distribution to estimate the true causal effect of an ad display. This “identification at infinity” approach alleviates the need for independent experimental randomization but results in nonstandard asymptotic theory, motivating our novel inference method. To validate our results, we use a set of large-scale randomized controlled experiments that Microsoft has run on its advertising platform. Our dataset has a large number of observations and a large number of variables and we employ LASSO to perform variable selection. Providing a basis for comparison with our estimates, we use a study conducted by Microsoft with approximately 9.3 million search sessions focusing on consumer click behavior across search result pages of a major search engine. Randomized experiments indicate that displaying a brand advertisement increases the probability of visiting the advertiser’s website by about 2.27 percentage points relative to a baseline visit rate of roughly 78 percent. Our non-experimental estimates exhibit broadly similar patterns to those obtained from randomized controlled trials, suggesting that the proposed observational estimator can recover qualitatively comparable treatment effects in large-scale advertising data.

  PublisherOA PDFDOI

• Statistical Inference of Optimal Allocations I: Regularities and their Implications

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?

  arXiv (Cornell University) · 2024-02-23 · 3 citations

  preprintOpen access

  The advent of generative AI (GenAI) technology produces transformative impact on the content creation landscape, offering alternative approaches to produce diverse, high-quality content across media, thereby reshaping online ecosystems but also raising concerns about market over-saturation and the potential marginalization of human creativity. Our work introduces a competition model generalized from the Tullock contest to analyze the tension between human creators and GenAI. Our theory and simulations suggest that despite challenges, a stable equilibrium between human and AI-generated content is possible. Our work contributes to understanding the competitive dynamics in the content creation industry, offering insights into the future interplay between human creativity and technological advancements in GenAI.

  PublisherOA PDFDOI

• The Econometrics of Static and Dynamic Models of Strategic Interactions

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  preprintOpen accessSenior author
  PublisherDOI

• The key role of absolute risk in the disclosure risk assessment of public data releases

  Proceedings of the National Academy of Sciences · 2024-03-05 · 1 citations

  letterOpen access

  Due to its small size and lifelong optical transparency, the fish Danionella cerebrum is an emerging model organism in biomedical research. How can this small vertebrate under 12 mm length produce sounds over 140 dB? We found that it possesses ...Motion is the basis of nearly all animal behavior. Evolution has led to some extraordinary specializations of propulsion mechanisms among invertebrates, including the mandibles of the dracula ant and the claw of the pistol shrimp. In contrast, vertebrate ...

  PublisherOA PDFDOI

• How Bad is Top-$K$ Recommendation under Competing Content Creators?

  arXiv (Cornell University) · 2023-02-03 · 3 citations

  preprintOpen access

  Content creators compete for exposure on recommendation platforms, and such strategic behavior leads to a dynamic shift over the content distribution. However, how the creators' competition impacts user welfare and how the relevance-driven recommendation influences the dynamics in the long run are still largely unknown. This work provides theoretical insights into these research questions. We model the creators' competition under the assumptions that: 1) the platform employs an innocuous top-$K$ recommendation policy; 2) user decisions follow the Random Utility model; 3) content creators compete for user engagement and, without knowing their utility function in hindsight, apply arbitrary no-regret learning algorithms to update their strategies. We study the user welfare guarantee through the lens of Price of Anarchy and show that the fraction of user welfare loss due to creator competition is always upper bounded by a small constant depending on $K$ and randomness in user decisions; we also prove the tightness of this bound. Our result discloses an intrinsic merit of the myopic approach to the recommendation, i.e., relevance-driven matching performs reasonably well in the long run, as long as users' decisions involve randomness and the platform provides reasonably many alternatives to its users.

  PublisherOA PDFDOI

• Sharp and Robust Estimation of Partially Identified Discrete Response Models

  arXiv (Cornell University) · 2023-10-03

  preprintOpen accessSenior author

  Semiparametric discrete choice models are widely used in a variety of practical applications. While these models are point identified in the presence of continuous covariates, they can become partially identified when covariates are discrete. In this paper we find that classical estimators, including the maximum score estimator, (Manski (1975)), loose their attractive statistical properties without point identification. First of all, they are not sharp with the estimator converging to an outer region of the identified set, (Komarova (2013)), and in many discrete designs it weakly converges to a random set. Second, they are not robust, with their distribution limit discontinuously changing with respect to the parameters of the model. We propose a novel class of estimators based on the concept of a quantile of a random set, which we show to be both sharp and robust. We demonstrate that our approach extends from cross-sectional settings to classical static and dynamic discrete panel data models.

  PublisherOA PDFDOI

• On uniform inference in nonlinear models with endogeneity

  Journal of Econometrics · 2022-04-08

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Balancing data privacy and usability in the federal statistical system

  Proceedings of the National Academy of Sciences · 2022 · 53 citations

  • Computer Science
  • Computer Security
  • Internet privacy

  The federal statistical system is experiencing competing pressures for change. On the one hand, for confidentiality reasons, much socially valuable data currently held by federal agencies is either not made available to researchers at all or only made available under onerous conditions. On the other hand, agencies which release public databases face new challenges in protecting the privacy of the subjects in those databases, which leads them to consider releasing fewer data or masking the data in ways that will reduce their accuracy. In this essay, we argue that the discussion has not given proper consideration to the reduced social benefits of data availability and their usability relative to the value of increased levels of privacy protection. A more balanced benefit-cost framework should be used to assess these trade-offs. We express concerns both with synthetic data methods for disclosure limitation, which will reduce the types of research that can be reliably conducted in unknown ways, and with differential privacy criteria that use what we argue is an inappropriate measure of disclosure risk. We recommend that the measure of disclosure risk used to assess all disclosure protection methods focus on what we believe is the risk that individuals should care about, that more study of the impact of differential privacy criteria and synthetic data methods on data usability for research be conducted before either is put into widespread use, and that more research be conducted on alternative methods of disclosure risk reduction that better balance benefits and costs.

  PublisherOA PDFDOI

• Learning from a Learning User for Optimal Recommendations

  arXiv (Cornell University) · 2022-02-03

  preprintOpen access

  In real-world recommendation problems, especially those with a formidably large item space, users have to gradually learn to estimate the utility of any fresh recommendations from their experience about previously consumed items. This in turn affects their interaction dynamics with the system and can invalidate previous algorithms built on the omniscient user assumption. In this paper, we formalize a model to capture such "learning users" and design an efficient system-side learning solution, coined Noise-Robust Active Ellipsoid Search (RAES), to confront the challenges brought by the non-stationary feedback from such a learning user. Interestingly, we prove that the regret of RAES deteriorates gracefully as the convergence rate of user learning becomes worse, until reaching linear regret when the user's learning fails to converge. Experiments on synthetic datasets demonstrate the strength of RAES for such a contemporaneous system-user learning problem. Our study provides a novel perspective on modeling the feedback loop in recommendation problems.

  PublisherOA PDFDOI

Recent grants

• AF: Medium: Collaborative Research: Econometric Inference and Algorithmic Learning in Games

  NSF · $446k · 2016–2023

• ICES: Large: Collaborative Research: Towards Realistic Mechanisms: statistics, inference, and approximation in simple Bayes-Nash implementation

  NSF · $138k · 2014–2016

Frequent coauthors

• Stephen Ryan

  Washington University in St. Louis

  31 shared

• Paul Novosad

  Dartmouth College

  28 shared

• Sam Asher

  Imperial College London

  27 shared

• Tatiana Komarova

  16 shared

• Vasilis Syrgkanis

  16 shared

• Jason D. Hartline

  Northwestern University

  16 shared

• Han Hong

  15 shared

• Patrick Bajari

  14 shared

Awards & honors

• H. Gregg Lewis fellowship, Duke University 2003 - 2008
• ACM EC Best Paper Award 2015
• US Patent US 2011/0313851 “A Tool for Analysis of Sponsored…