arbiter
JournalsAccount

Decision Theory for Treatment Choice Problems with Partial Identification

José Luis Montiel Olea et al.

The Review of Economic Studies2026https://doi.org/10.1093/restud/rdag015article
FT50AJG 4*ABDC A*
Weight
0.50

Abstract

We apply classical statistical decision theory to a large class of treatment choice problems with partial identification. We show that, in a general class of problems with Gaussian likelihood, all decision rules are admissible; it is maximin-welfare optimal to ignore all data; and, for severe enough partial identification, there are infinitely many minimax-regret optimal decision rules, all of which sometimes randomize the policy recommendation. We uniquely characterize the minimax-regret optimal rule that least frequently randomizes, and show that, in some cases, it can outperform other minimax-regret optimal rules in terms of what we call profiled regret. We analyse the implications of our results in the aggregation of experimental estimates for policy adoption, extrapolation of Local Average Treatment Effects, and policy making in the presence of omitted variable bias.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1093/restud/rdag015

Or copy a formatted citation

@article{josé2026,
  title        = {{Decision Theory for Treatment Choice Problems with Partial Identification}},
  author       = {José Luis Montiel Olea et al.},
  journal      = {The Review of Economic Studies},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1093/restud/rdag015},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Decision Theory for Treatment Choice Problems with Partial Identification

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.