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Patrick Rehill

Australian National University (from arXiv:2404.13356, 2024) · ORCID · OpenAlex

7 papers in scope · 4 published · 5 on the econ.EM arXiv · 34 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. Nicholas Biddle
  2. Bart Cockx
  3. Joost Bollens
  4. Michael Lechner
  5. Ulrike Unterhofer
  6. Conny Wunsch
  7. Michael C. Knaus
  8. Phillip Heiler
  9. Esther Duflo
  10. Gabriel Okasa
  11. Noémi Kreif
  12. Mert Demirer
  13. Julia Hatamyar
  14. Anthony Strittmatter
  15. Riccardo Di Francesco
  16. Victor Chernozhukov
  17. Iván Fernández-Val
  18. Daniel Jacob
  19. Susan Athey
  20. Ehsanul Bashar Pranto

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

(5 of 7)

Understanding the effect of stay-at-home orders on psychological distress during the COVID-19 pandemic: Evidence from a longitudinal study in Australia
published2025 · PLoS ONE
with Roy Barnes, Nicholas Biddle, Ben Edwards
PLoS ONE
How Do Applied Researchers Use the Causal Forest? A Methodological Review
published2025 · International Statistical Review · 18 citations
International Statistical Review
Policy Learning for Many Outcomes of Interest: Combining Optimal Policy Trees with Multi-objective Bayesian Optimisation
published2024 · Computational Economics · 1 citations · first circulated 2022
with Nicholas Biddle
Paper profile Computational Economics arXiv:2212.06312
Distilling interpretable causal trees from causal forests
working paper2024 · arXiv
Paper profile arXiv arXiv:2408.01023
How do applied researchers use the Causal Forest? A methodological review of a method
working paper2024 · arXiv · 3 citations
Paper profile arXiv arXiv:2404.13356
Transparency challenges in policy evaluation with causal machine learning: improving usability and accountability
published2024 · Data & Policy · 12 citations · first circulated 2023
with Nicholas Biddle
Paper profile Data & Policy arXiv:2310.13240
Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making
working paper2023 · arXiv
with Nicholas Biddle
Paper profile arXiv arXiv:2309.00805

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.