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Peer-reviewedLicence not statedManagement Science

Learning Optimal Prescriptive Trees from Observational Data

  • Nathanael Jo1
  • Sina Aghaei2
  • Andrés Gómez2
  • Phebe Vayanos3,4
4 affiliations
  1. 1Center for Artificial Intelligence in Society, University of Southern California, Los Angeles, California 90089
  2. 2Center for Artificial Intelligence in Society, University of Southern California, Los Angeles, California 90089; and Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, California 90089
  3. 3Center for Artificial Intelligence in Society, University of Southern California, Los Angeles, California 90089; and Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, California 90089; and Department of Computer Science, University of Southern California, Los Angeles, California 90089
  4. 4University of Southern California
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Cite

BibTeXRIS
BibTeX
@article{jo2026learning,
  title = {{Learning Optimal Prescriptive Trees from Observational Data}},
  author = {Nathanael Jo and Sina Aghaei and Andrés Gómez and Phebe Vayanos},
  journal = {Management Science},
  year = {2026},
  month = jul,
  doi = {10.1287/mnsc.2021.02813},
  url = {http://arxiv.org/abs/2108.13628},
}
RIS
TY  - JOUR
TI  - Learning Optimal Prescriptive Trees from Observational Data
AU  - Nathanael Jo
AU  - Sina Aghaei
AU  - Andrés Gómez
AU  - Phebe Vayanos
PY  - 2026
DA  - 2026/07/30
JO  - Management Science
DO  - 10.1287/mnsc.2021.02813
UR  - http://arxiv.org/abs/2108.13628
SN  - 0025-1909
SN  - 1526-5501
ER  -