Privacy-preserving Quantile Treatment Effect Estimation for Randomized Controlled Trials

2024/01/25 by Leon Yao, Yao, Leon, Paul Yiming Li +3 · 2 citations
Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Inference #Statistical Methods in Clinical Trials

paper · pdf · doi:10.48550/arxiv.2401.14549

openalex publication_date 2024/01/25 · openalex created_date 2024/01/30 · openalex updated_date 2026/07/28

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.

Cited by

• PrivATE: Differentially Private Confidence Intervals for Average Treatment Effects
• Model Agnostic Differentially Private Causal Inference

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