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In "Differential Perspectives: Epistemic Disconnects Surrounding the USCensus Bureau's Use of Differential Privacy," boyd and Sarathy argue thatempirical evaluations of the Census Disclosure Avoidance System (DAS),including our published analysis, failed to recognize how the benchmark dataagainst which the 2020 DAS was evaluated is never a ground truth of populationcounts. In this commentary, we explain why policy evaluation, which was themain goal of our analysis, is still meaningful without access to a perfectground truth. We also point out that our evaluation leveraged features specificto the decennial Census and redistricting data, such as block-level populationinvariance under swapping and voter file racial identification, betterapproximating a comparison with the ground truth. Lastly, we show that accuratestatistical predictions of individual race based on the Bayesian ImprovedSurname Geocoding, while not a violation of differential privacy, substantiallyincreases the disclosure risk of private information the Census Bureau soughtto protect. We conclude by arguing that policy makers must confront a keytrade-off between data utility and privacy protection, and an epistemicdisconnect alone is insufficient to explain disagreements between policychoices.

Comment: The Essential Role of Policy Evaluation for the 2020 Census Disclosure Avoidance System