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Shosei Sakaguchi

The University of Tokyo (from arXiv:2605.02414, 2026) · ORCID · OpenAlex

10 papers in scope · 5 published · 6 on the econ.EM arXiv · 44 citations · h-index 4 (over the papers listed here)

Related authors

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

  1. Toru Kitagawa
  2. Aleksey Tetenov
  3. Yagan Hazard
  4. Henry Zhu
  5. Emma Brunskill
  6. Jeff Rowley
  7. JoonHwan Cho
  8. Rohit Kumar
  9. Sukjin Han
  10. Yanbo Liu
  11. Nan Liu
  12. Yifan Cui
  13. Chen Qiu
  14. Guanyi Wang
  15. Hugo Enrique Sánchez López
  16. Thomas M. Russell
  17. Martin Thyrsgaard
  18. Guanyi Wang
  19. Xi Chen
  20. Yuanyuan Wan

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

(7 of 10)

working paper2026 · arXiv
Identification and Bayesian Inference for Synthetic Control Methods with Spillover Effects
published2026 · Econometrics Journal
working paper2025 · arXiv
published2025 · Quantitative Economics · 7 citations · first circulated 2021
working paper2024 · arXiv · 1 citations
working paper2024 · arXiv
published2023 · Journal of Applied Econometrics · first circulated 2019
working paper2021 · arXiv · 27 citations
Estimation of average treatment effects using panel data when treatment effect heterogeneity depends on unobserved fixed effects
published2020 · Journal of Applied Econometrics · 4 citations · first circulated 2019
Estimation of time-varying average treatment effects using panel data when unobserved fixed effects affect potential outcomes differently
published2016 · Economics Letters · 5 citations

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.