Estimating causal effects from observational data is essential in fields such as medicine, economics and social sciences, where privacy concerns are paramount. We propose a general, model-agnostic framework for differentially private estimation of average treatment effects (ATE) that avoids strong structural assumptions on the data-generating process or the models used to estimate propensity scores and conditional outcomes. In contrast to prior work, which enforces differential privacy by directly privatizing these nuisance components, our approach decouples nuisance estimation from privacy protection. This separation allows the use of flexible, state-of-the-art black-box models, while differential privacy is achieved by perturbing only predictions and aggregation steps within a fold-splitting scheme with ensemble techniques. We instantiate the framework for three classical estimators -- the G-Formula, inverse propensity weighting (IPW), and augmented IPW (AIPW) -- and provide formal utility and privacy guarantees, together with privatized confidence intervals. Empirical results on synthetic and real data show that our methods maintain competitive performance under realistic privacy budgets.

翻译：从观测数据中估计因果效应在医学、经济学和社会科学等领域至关重要，这些领域对隐私保护的要求极高。我们提出了一种通用的、模型无关的框架，用于平均处理效应（ATE）的差分隐私估计，该框架避免了对数据生成过程或用于估计倾向得分与条件结果的模型施加过强的结构性假设。与先前通过直接对干扰项进行隐私化处理来强制差分隐私的工作不同，我们的方法将干扰项估计与隐私保护解耦。这种分离允许使用灵活的、最先进的黑盒模型，而差分隐私则通过在一个结合集成技术的折分方案中，仅对预测和聚合步骤进行扰动来实现。我们针对三种经典估计器——G-Formula、逆倾向加权（IPW）和增强的逆倾向加权（AIPW）——实例化了该框架，并提供了正式的效用与隐私保证，以及隐私化的置信区间。在合成数据和真实数据上的实证结果表明，我们的方法在实际的隐私预算下保持了有竞争力的性能。