arXiv 1 Apr 2026 · Statistics — Methodology
arXiv:2604.00504 · PDF · DOI · OpenAlex · Extracted main text
Attrition in survey and field experiments presents a challenge for social science research. Common approaches to deal with this problem -- such as complete case analysis, multiple imputation, and weighting methods -- rely on strong assumptions that may not hold in practice. This paper introduces a new method that combines recent advances in statistical inference with established tools for handling missing data. The approach produces prediction intervals for treatment effects that are both robust and precise. Evidence from simulation studies shows that the method achieves better coverage and produces narrower intervals than common alternatives. The reanalysis of two recently published experiment studies illustrates how this framework allows researchers to compare treatment effects across participants who remain in the study, those who drop out, and the full sample. Taken together, these results highlight how the proposed approach provides a stronger foundation for causal inference in the presence of attrition.
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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 | Huber, Martin (2012) Identification of Average Treatment Effects in Social Experiments Under Alternative Forms of Attrition | 1.000 | 6 | 3 | 100% |
| 2 | Lei, Lihua and Candès, Emmanuel J (2021) Conformal Inference of Counterfactuals and Individual Treatment Effects | 0.969 | 22 | 7 | 91% |
| 3 | Gao, Chenyin and Gilbert, Peter B. and Han, Larry (2025) On the Role of Surrogates in Conformal Inference of Individual Causal Effects | 0.956 | 16 | 5 | 88% |
| 4 | Yang, Yachong and Kuchibhotla, Arun Kumar and Tchetgen Tchetgen, Eric (2024) Doubly Robust Calibration of Prediction Sets under Covariate Shift | 0.874 | 9 | 5 | 67% |
| 5 | Coppock, Alexander and Gerber, Alan S. and Green, Donald P. and Kern… (2017) Combining Double Sampling and Bounds to Address Nonignorable Missing Outcomes in Randomized Experiments | 0.874 | 5 | 2 | 100% |
| 6 | King, Gary and Honaker, James and Joseph, Anne and Scheve, Kenneth (2001) Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation | 0.843 | 3 | 3 | 100% |
| 7 | Finkel, Steven E. and Neundorf, Anja and Rascón Ramírez, Ericka (2024) Can Online Civic Education Induce Democratic Citizenship? Experimental Evidence from a New Democracy | 0.830 | 7 | 2 | 86% |
| 8 | Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/Debiased Machine Learning for Treatment and Structural Parameters | 0.737 | 4 | 3 | 50% |
| 9 | Lei, Jing and Wasserman, Larry (2014) Distribution-Free Prediction Bands for Non-parametric Regression | 0.737 | 3 | 2 | 100% |
| 10 | Margalit, Yotam and Shayo, Moses (2021) How Markets Shape Values and Political Preferences: A Field Experiment | 0.693 | 10 | 1 | 100% |
Showing the top 10 of 56 scored citations.