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Randomized control trials, RCTs, have become a powerful tool for assessingthe impact of interventions and policies in many contexts. They are consideredthe gold-standard for inference in the biomedical fields and in many socialsciences. Researchers have published an increasing number of studies that relyon RCTs for at least part of the inference, and these studies typically includethe response data collected, de-identified and sometimes protected throughtraditional disclosure limitation methods. In this paper, we empirically assessthe impact of strong privacy-preservation methodology (with \ac{DP}guarantees), on published analyses from RCTs, leveraging the availability ofreplication packages (research compendia) in economics and policy analysis. Weprovide simulations studies and demonstrate how we can replicate the analysisin a published economics article on privacy-protected data under variousparametrizations. We find that relatively straightforward DP-based methodsallow for inference-valid protection of the published data, thoughcomputational 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 strongprivacy protection.

Assessing Utility of Differential Privacy for RCTs