Xinran Li, Peizan Sheng, Zeyang Yu
arXiv 1 Jul 2025 · Econometrics · 1 citations (OpenAlex)
arXiv:2507.00795 · PDF · DOI · OpenAlex · Extracted main text
Although appealing, randomization inference for treatment effects can suffer from severe size distortion due to sample attrition. We propose new, computationally efficient methods for randomization inference that remain valid under a range of potentially informative missingness mechanisms. We begin by constructing valid p-values for testing sharp null hypotheses, using the worst-case p-value from the Fisher randomization test over all possible imputations of missing outcomes. Leveraging distribution-free test statistics, this worst-case p-value admits a closed-form solution, connecting naturally to bounds in the partial identification literature. Our test statistics incorporate both potential outcomes and missingness indicators, allowing us to exploit structural assumptions-such as monotone missingness-for increased power. We further extend our framework to test non-sharp null hypotheses concerning quantiles of individual treatment effects. The methods are illustrated through simulations and an empirical application.
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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 | Zhang, J. L. and D. B. Rubin (2003) Estimation of causal effects via principal stratification when some outcomes are truncated by “death” | 1.000 | 11 | 4 | 100% |
| 2 | Lee, D. S (2009) Training, wages, and sample selection: Estimating sharp bounds on treatment effects | 1.000 | 10 | 5 | 100% |
| 3 | Fisher, R. A (1935) Design of experiments | 0.928 | 4 | 4 | 100% |
| 4 | Caughey, D., A. Dafoe, X. Li, and L. Miratrix (2023) Randomisation inference beyond the sharp null: bounded null hypotheses and quantiles of individual treatment effects | 0.920 | 9 | 5 | 78% |
| 5 | Wang, W (2015) Exact optimal confidence intervals for hypergeometric parameters | 0.874 | 6 | 2 | 100% |
| 6 | Heckman, J (1974) Shadow prices, market wages, and labor supply | 0.843 | 3 | 3 | 100% |
| 7 | Horowitz, J. L. and C. F. Manski (2000) Nonparametric analysis of randomized experiments with missing covariate and outcome data | 0.811 | 4 | 2 | 100% |
| 8 | Duflo, E., R. Glennerster, and M. Kremer (2007) Using randomization in development economics research: A toolkit | 0.737 | 3 | 2 | 100% |
| 9 | Gerber, A. S. and D. P. Green (2012) Field Experiments: Design, Analysis, and Interpretation | 0.737 | 3 | 2 | 100% |
| 10 | Manski, C. F (1990) Nonparametric bounds on treatment effects | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 49 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Testing Mechanisms | 0.585 | 3 | 1 |
| 2 | Randomization Inference For the Always-Reporter Average Treatment Effect | 0.405 | 1 | 1 |