Differentially Private Two-Stage Empirical Risk Minimization with Applications to Individualized Treatment Rule
差分隐私两阶段经验风险最小化及其个体化治疗规则应用
Differential privacy provides a formal framework for releasing statistical estimators that limit how much any single observation can influence the output, by injecting calibrated random noise. We study differentially private estimation in two-stage procedures common in causal inference and individualized treatment rule (ITR) learning, in which data-dependent weights are first estimated to enforce covariate balance and a parameter of interest is then obtained by weighted…