PeopleofSuperintelligence

Researcher

Martin Huber

Martin Huber is an economist whose work applies machine learning to causal analysis, policy evaluation and market oversight. He earned master's degrees in economics and international business at Innsbruck and completed a doctorate in 2010; he served as an assistant professor until 2014 and was a professor at Fribourg by 2014. He received the University Latsis Prize for econometric research in 2014 and held a research stay at the University of Sydney in 2019. His 2023 work included a book on causal analysis, a coupon study using causal machine learning and a deep-learning method for cartel detection; in 2025, he published an impact-evaluation book and coauthored a graph-attention approach to collusive bidding. In February 2026, he and Sarina Joy Oberhänsli proposed a difference-in-differences framework using double machine learning; as of October 2026, he is Professor of Applied Econometrics and Policy Evaluation at the University of Fribourg.

Born
1980
Works
435

Years link to their sources. Publication numbers from OpenAlex, as of 22 October 2026. Citations and h-index are hidden because this OpenAlex record may be incomplete or mix in other authors.

Timeline

  1. 2004–2010

    Economics and finance degrees

    He earns master's degrees in economics and international business studies at the University of Innsbruck in 2004. He completes a Ph.D. in Economics and Finance at the University of St. Gallen in 2010.

  2. 2010–2014

    Assistant professor at St. Gallen

    After completing his Ph.D. in 2010, he serves as an Assistant Professor at the University of St. Gallen until 2014. Research stays take him to Harvard University in 2011 and 2012 and the University of Sydney in 2014.

  3. 2014

    Fribourg professorship and Latsis prize

    By 2014, he is Professor of Applied Econometrics and Policy Evaluation at the University of Fribourg. In 2014, he receives the University Latsis Prize for research in econometrics.

  4. 2019

    A Sydney research stay

    He undertakes a research stay at the University of Sydney in 2019.

  5. 2023

    Causal analysis and coupon effects

    In 2023, he publishes Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R with MIT Press. With Henrika Langen, he applies causal machine learning to estimate a coupon campaign's effect on retailer sales.

  6. 2023

    Deep learning for cartel detection

    In 2023, with David Imhof, he develops a deep-learning method to detect cartels and price fixing in public tenders. Tests on procurement data from Switzerland and Japan correctly classify 19 of 20 firms as cartel participants or competitors.

  7. 2025

    Impact evaluation and graph networks

    In 2025, he publishes Impact Evaluation in Firms and Organizations: With Applications in R and Python with MIT Press. The book guides evaluation of company interventions and covers machine-learning methods. In 2025, he coauthors Catching Bid-rigging Cartels with Graph Attention Neural Networks, which uses graph attention networks to detect collusive behavior.

  8. As of October 2026

    Professor of applied econometrics

    As of October 2026, he is Professor of Applied Econometrics and Policy Evaluation in the University of Fribourg's Department of Economics. In February 2026, he and Sarina Joy Oberhänsli propose a difference-in-differences framework with mediation using double machine learning.

Career

  1. NowUniversity of FribourgProfessor of Applied Econometrics and Policy Evaluation
  2. 2014–2019University of Sydney
  3. 2011–2012Harvard University
  4. 2010–2014University of St.GallenAssistant Professor

Education

  1. 2010University of St.GallenPhD in Economics and Finance
  2. 2004Universität InnsbruckMaster's degrees in economics and international business studies

From the biography.

  1. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Harvard University. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  2. The median h-index is 48 at universities and 14 at frontier AI labsIncludes Harvard University. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  3. Companies supply the most prior-work hires at 7 of the 9 frontier AI labsIncludes Harvard University. Our estimates of the organizations where 550 people worked directly before joining 9 frontier AI labs across 620 career transitions, comparing the main feeder organizations per lab.
  4. 44% of AI career moves go from one university to anotherIncludes Harvard University. We estimate how 26,439 AI career moves link universities, companies, labs, nonprofits and government research centers, with decade counts, net flows and the top paths.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. Prof. Martin Huber | 主講人簡介 econ.sinica.edu.tw. Checked 21 October 2026.
  2. Martin Huber | Chair of Applied Econometrics and Policy Evaluation | University of Fribourg unifr.ch. Checked 21 October 2026.
  3. Un Prix Latsis de l’Université de Genève récompense un jeune chercheur fribourgeois unifr.ch. Checked 21 October 2026.
  4. How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign | PLOS One journals.plos.org. Checked 21 October 2026.
  5. Artificial intelligence for cartel detection | Chair of Applied Econometrics and Policy Evaluation | University of Fribourg unifr.ch. Checked 21 October 2026.
  6. Impact Evaluation in Firms and Organizations by Martin Huber: 9780262552929 | PenguinRandomHouse.com: Books penguinrandomhouse.com. Checked 21 October 2026.
  7. Catching Bid-rigging Cartels with Graph Attention Neural Networks arxiv.org. Checked 21 October 2026.
  8. Difference-in-differences for mediation analysis using double machine learning arxiv.org. Checked 21 October 2026.