Differentially Private Tests of Fisher's Sharp Null Hypothesis for Binary Outcomes
二元结局下 Fisher 尖锐零假设的差分隐私检验
Across many disciplines, causal inference often relies on randomized experiments with binary outcomes. In such experiments, often analysts are interested in testing Fisher's sharp null hypothesis, i.e., the treatment has zero effect on the outcome for every study subject. Sometimes the outcomes are sensitive and must be kept confidential, for example, when they comprise physical or mental health measurements. Releasing test statistics or p-values computed with the…