The average treatment effect (ATE) is widely used to evaluate the effectiveness of drugs and other medical interventions. In safety-critical applications like medicine, reliable inferences about the ATE typically require valid uncertainty quantification, such as through confidence intervals (CIs). However, estimating treatment effects in these settings often involves sensitive data that must be kept private. In this work, we present PrivATE, a novel machine learning framework for computing CIs for the ATE under differential privacy. Specifically, we focus on deriving valid privacy-preserving CIs for the ATE from observational data. Our PrivATE framework consists of three steps: (i) estimating the differentially private ATE through output perturbation; (ii) estimating the differentially private variance in a doubly robust manner; and (iii) constructing the CIs while accounting for the uncertainty from both the estimation and privatization steps. Our PrivATE framework is model agnostic, doubly robust, and ensures valid CIs. We demonstrate the effectiveness of our framework using synthetic and real-world medical datasets. To the best of our knowledge, we are the first to derive a general, doubly robust framework for valid CIs of the ATE under ($\varepsilon,\delta$)-differential privacy.

翻译：平均处理效应（ATE）被广泛用于评估药物及其他医学干预措施的有效性。在医学等安全关键应用中，对ATE的可靠推断通常需要有效的不确定性量化，例如通过置信区间（CI）。然而，在这些场景中估计处理效应往往涉及必须保持私密的敏感数据。本文提出PrivATE，一种新颖的机器学习框架，用于在差分隐私约束下计算ATE的置信区间。具体而言，我们专注于从观测数据中推导出有效的隐私保护ATE置信区间。我们的PrivATE框架包含三个步骤：（i）通过输出扰动估计差分隐私ATE；（ii）以双重稳健方式估计差分隐私方差；（iii）构建置信区间时同时考虑估计步骤与隐私化步骤的不确定性。该框架具有模型无关性、双重稳健性，并能确保置信区间的有效性。我们通过合成与真实医疗数据集验证了框架的有效性。据我们所知，这是首个在（$\varepsilon,\delta$）-差分隐私约束下为ATE构建通用、双重稳健的有效置信区间的框架。