arXiv 26 Mar 2026 · Econometrics
arXiv:2603.24970 · PDF · DOI · OpenAlex · Extracted main text
This article studies randomization inference for treatment effects in randomized controlled trials with attrition, where outcomes are observed for only a subset of units. We assume monotonicity in reporting behavior as in \cite{lee2009training} and focus on the average treatment effect for always-reporters (AR-ATE), defined as units whose outcomes are observed under both treatment and control. Because always-reporter status is only partially revealed by observed assignment and response patterns, we propose a worst-case randomization test that maximizes the randomization p-value over all always-reporter configurations consistent with the data, with an optional pretest to prune implausible configurations. Using studentized Hajek- and chi-square-type statistics, we show the resulting procedure is finite-sample valid for the sharp null and asymptotically valid for the weak null. We also discuss computational implementations for discrete outcomes and integer-programming-based bounds for continuous outcomes.
appendix boundary found by appendix_command · 26% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Lee, David S (2009) Training, wages, and sample selection: Estimating sharp bounds on treatment effects | 1.000 | 11 | 4 | 100% |
| 2 | Imbens, Guido W and Rubin, Donald B (2015) Causal inference in statistics, social, and biomedical sciences | 0.737 | 3 | 3 | 67% |
| 3 | Aronow, PM and Chang, Haoge and Lopatto, Patrick (2024) Randomization-based confidence sets for the local average treatment effect self | 0.511 | 2 | 2 | 50% |
| 4 | Wu, Jason and Ding, Peng (2021) Randomization tests for weak null hypotheses in randomized experiments | 0.511 | 2 | 2 | 50% |
| 5 | Imbens, Guido W and Manski, Charles F (2004) Confidence intervals for partially identified parameters | 0.511 | 2 | 1 | 100% |
| 6 | Semenova, Vira (2025) Generalized lee bounds | 0.511 | 2 | 1 | 100% |
| 7 | Stoye, Jörg (2009) More on confidence intervals for partially identified parameters | 0.511 | 2 | 1 | 100% |
| 8 | Vira Semenova (2025) Generalized Lee bounds | 0.405 | 1 | 1 | 100% |
| 9 | Berger, Roger L and Boos, Dennis D (1994) P values maximized over a confidence set for the nuisance parameter | 0.405 | 1 | 1 | 100% |
| 10 | Canay, Ivan A and Romano, Joseph P and Shaikh, Azeem M (2017) Randomization tests under an approximate symmetry assumption | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 32 scored citations.