  Amidst rising appreciation for privacy and data usage rights, researchers
have increasingly acknowledged the principle of data minimization, which holds
that the accessibility, collection, and retention of subjects' data should be
kept to the bare amount needed to answer focused research questions. Applying
this principle to randomized controlled trials (RCTs), this paper presents
algorithms for making accurate inferences from RCTs under stringent data
retention and anonymization policies. In particular, we show how to use
recursive algorithms to construct running estimates of treatment effects in
RCTs, which allow individualized records to be deleted or anonymized shortly
after collection. Devoting special attention to non-i.i.d. data, we further
show how to draw robust inferences from RCTs by combining recursive algorithms
with bootstrap and federated strategies.
