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arXiv · 2401.14549

Privacy-preserving Quantile Treatment Effect Estimation for Randomized Controlled Trials

Leon Yao·Paul Yiming Li·Jiannan Lu
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Abstract

In accordance with the principle of "data minimization", many internet companies are opting to record less data. However, this is often at odds with A/B testing efficacy. For experiments with units with multiple observations, one popular data minimizing technique is to aggregate data for each unit. However, exact quantile estimation requires the full observation-level data. In this paper, we develop a method for approximate Quantile Treatment Effect (QTE) analysis using histogram aggregation. In addition, we can also achieve formal privacy guarantees using differential privacy.

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Leon Yao, Paul Yiming Li, Jiannan Lu. 2024-01-25. Privacy-preserving Quantile Treatment Effect Estimation for Randomized Controlled Trials. https://arxiv.org/abs/2401.14549

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