EconBase
← All authors

David Imhof

University of Fribourg (per OpenAlex) · OpenAlex

6 papers in scope · 5 published · 1 on the econ.EM arXiv · 145 citations · h-index 3 (over the papers listed here)

Related authors

The 20 authors closest to this one in our weighted citation graph, most related first.

  1. Alexey Zaytsev
  2. Lucas Carvalho Gomes
  3. Hannes Wallimann
  4. Jannis Kueck
  5. Martin Spindler
  6. Martin Huber
  7. Victor Chernozhukov
  8. Philipp Bach
  9. Sven Klaaßen
  10. Christian Hansen
  11. Lukáš Lafférs
  12. Kevin Kloiber
  13. Nicolas Apfel
  14. Alexandre Belloni
  15. Malte S. Kurz
  16. Vasilis Syrgkanis
  17. Pedro H. C. Sant’Anna
  18. Julia Hatamyar
  19. Mara Mattes
  20. Whitney K. Newey

Proximity is measured over citations between two papers we both hold, weighted by how heavily one leans on the other, and is symmetric — it does not distinguish citing from being cited. Authors without a profile here are skipped, and a genuinely close colleague can be missing simply because their work is not in our arXiv corpus. Method: docs/06-citations-pipeline.md.

Papers

(2 of 6)

working paper2025 · arXiv
Flagging cartel participants with deep learning based on convolutional neural networks
published2023 · 11 citations · first circulated 2021
with Martin Huber, D. Imhof
published2022 · Computational Economics · 2 citations
Transnational Machine Learning with Screens for Flagging Bid-Rigging Cartels
published2022 · Journal of the Royal Statistical Society Series A (Statistics in Society) · 27 citations
with Martin Huber, Rieko Ishii
Detecting bid-rigging coalitions in different countries and auction formats
published2021 · International Review of Law and Economics · 3 citations
with Hannes Wallimann, D. Imhof
Machine learning with screens for detecting bid-rigging cartels
published2019 · 102 citations · first circulated 2018

Assembled from arXiv and OpenAlex. Duplicate records for the same paper are merged, and the published version is shown where we could identify one. Corrections welcome.