Soumya Mukherjee, Aratrika Mustafi, Aleksandra Slavković, Lars Vilhuber
arXiv 26 Sep 2023 · Statistics — Applications
arXiv:2309.14581 · PDF · DOI · OpenAlex · Extracted main text
Randomized control trials, RCTs, have become a powerful tool for assessing the impact of interventions and policies in many contexts. They are considered the gold-standard for inference in the biomedical fields and in many social sciences. Researchers have published an increasing number of studies that rely on RCTs for at least part of the inference, and these studies typically include the response data collected, de-identified and sometimes protected through traditional disclosure limitation methods. In this paper, we empirically assess the impact of strong privacy-preservation methodology (with \ac{DP} guarantees), on published analyses from RCTs, leveraging the availability of replication packages (research compendia) in economics and policy analysis. We provide simulations studies and demonstrate how we can replicate the analysis in a published economics article on privacy-protected data under various parametrizations. We find that relatively straightforward DP-based methods allow for inference-valid protection of the published data, though computational issues may limit more complex analyses from using these methods. The results have applicability to researchers wishing to share RCT data, especially in the context of low- and middle-income countries, with strong privacy protection.
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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 | Blattman, Christopher and Jamison, Julian C. and Sheridan, Margaret Replication data for: Reducing Crime and Violence: Experimental Evidence from Cognitive Behavioral Therapy in Liberia | 0.950 | 7 | 4 | 86% |
| 2 | Dwork, Cynthia and McSherry, Frank and Nissim, Kobbi and Smith, Adam (2006) Calibrating Noise to Sensitivity in Private Data Analysis | 0.843 | 3 | 3 | 100% |
| 3 | Vishesh Karwa and Salil Vadhan (2017) Finite Sample Differentially Private Confidence Intervals | 0.811 | 15 | 3 | 53% |
| 4 | Blattman, Christopher and Jamison, Julian C. and Sheridan, Margaret (2017) Reducing Crime and Violence: Experimental Evidence from Cognitive Behavioral Therapy in Liberia | 0.763 | 9 | 4 | 44% |
| 5 | Dunn, Peter K. and Smyth, Gordon K (2018) Generalized linear models with examples in R | 0.737 | 3 | 2 | 100% |
| 6 | Stanley L. Warner (1965) Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias | 0.644 | 2 | 2 | 100% |
| 7 | Meager, Rachael (2019) Understanding the Average Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of Seven Randomized Experiments | 0.585 | 3 | 1 | 100% |
| 8 | Slavković, Aleksandra and Seeman, Jeremy (2023) Statistical Data Privacy: A Song of Privacy and Utility self | 0.585 | 3 | 1 | 100% |
| 9 | Roth, Jonathan (2022) Pretest with Caution: Event-Study Estimates after Testing for Parallel Trends | 0.511 | 2 | 1 | 100% |
| 10 | Committee for Medicinal Products for Human Use (2015) Guideline on adjustment for baseline covariates in clinical trials | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 51 scored citations.