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Randomization-Based Confidence Sets for the Local Average Treatment Effect

P M Aronow et al.

Biometrika2026https://doi.org/10.1093/biomet/asag010article
AJG 4ABDC A*
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0.50

What the paper says

Summary We consider the problem of generating confidence sets in randomized experiments with noncompliance. We show that a refinement of a randomization-based procedure proposed by Imbens & Rosenbaum (2005) has desirable properties. Specifically, we show that using a studentized Anderson–Rubin statistic as a test statistic yields confidence sets that are finite-sample exact under treatment effect homogeneity and remain asymptotically valid for the local average treatment effect when the treatment effects are heterogeneous. We provide a uniform analysis of this procedure and efficient algorithms to construct the confidence sets.

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https://doi.org/https://doi.org/10.1093/biomet/asag010

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@article{p2026,
  title        = {{Randomization-Based Confidence Sets for the Local Average Treatment Effect}},
  author       = {P M Aronow et al.},
  journal      = {Biometrika},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1093/biomet/asag010},
}

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Randomization-Based Confidence Sets for the Local Average Treatment Effect

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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.