  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.
