在保护隐私的前提下，精准计算药物疗效的置信区间。
PrivATE: Differentially Private Confidence Intervals for Average Treatment Effects
- 通过输出扰动和双重稳健估计，实现隐私保护下的平均治疗效应估算。
- 构建置信区间时同时考虑估计与隐私化带来的不确定性，保证有效性。
- 适用于医疗等敏感数据场景，支持任意模型且结果可靠。
平均治疗效应（ATE）广泛用于评估药物及其他医疗干预的效果。在医疗等安全关键领域，对ATE的可靠推断通常需要有效的不确定性量化，例如置信区间（CIs）。然而，此类分析常涉及需严格保密的敏感数据。本文提出PrivATE，一种新的机器学习框架，可在($\varepsilon,δ$)-差分隐私下计算ATE的置信区间。该框架包含三个步骤：(i) 通过输出扰动估计差分隐私化的ATE；(ii) 以双重稳健方式估计差分隐私化的方差；(iii) 在构建置信区间时同时考虑估计与隐私化引入的不确定性。PrivATE具有模型无关性、双重稳健性，并确保置信区间有效性。我们在合成数据和真实医疗数据集上验证了其有效性。据我们所知，这是首个在($\varepsilon,δ$)-差分隐私下，针对观测数据的通用、双重稳健的ATE置信区间构造框架。
原文摘要 · Abstract (English)
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 ($\varepsilon,δ$)-differential privacy.
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