<ul><li>PrivATE is a novel machine learning framework for computing confidence intervals (CIs) for the average treatment effect (ATE) under differential privacy.</li><li>The framework focuses on deriving valid privacy-preserving CIs for the ATE from observational data in sensitive settings such as medicine.</li><li>PrivATE consists of three steps: estimating a differentially private ATE, estimating the differentially private variance, and constructing CIs while considering uncertainty from both estimation and privatization steps.</li><li>This framework is model agnostic, doubly robust, and ensures valid CIs, demonstrated through synthetic and real-world medical datasets, marking a significant advancement in valid CIs for ATE under differential privacy.</li></ul>

PrivATE: Differentially Private Confidence Intervals for Average Treatment Effects

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