# Differentially Private Multi-Site Treatment Effect Estimation

arXiv: https://arxiv.org/abs/2310.06237 · HTML: https://mlsanity.com/paper/2310.06237/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 44 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 19 of 19)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning](https://mlsanity.com/paper/1702.07464/) (2017)
- [Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning](https://mlsanity.com/paper/1812.00535/) (2018)
- [Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity](https://mlsanity.com/paper/1811.12469/) (2018)
- [Distributed Differential Privacy via Shuffling](https://mlsanity.com/paper/1808.01394/) (2018)
- [The Privacy Blanket of the Shuffle Model](https://mlsanity.com/paper/1903.02837/) (2019)
- [On Safeguarding Privacy and Security in the Framework of Federated Learning](https://mlsanity.com/paper/1909.06512/) (2019)
- [Prochlo: Strong Privacy for Analytics in the Crowd](https://mlsanity.com/paper/1710.00901/) (2017)
- [Scalable and Differentially Private Distributed Aggregation in the Shuffled Model](https://mlsanity.com/paper/1906.08320/) (2019)
- [Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead](https://mlsanity.com/paper/2106.04247/) (2021)
- [Pure Differentially Private Summation from Anonymous Messages](https://mlsanity.com/paper/2002.01919/) (2020)
- [Differentially Private Estimation of Heterogeneous Causal Effects](https://mlsanity.com/paper/2202.11043/) (2022)
- [Private Causal Inference](https://mlsanity.com/paper/1512.05469/) (2015)
- [Privacy-Preserving Causal Inference via Inverse Probability Weighting](https://mlsanity.com/paper/1905.12592/) (2019)
- [On Distributed Differential Privacy and Counting Distinct Elements](https://mlsanity.com/paper/2009.09604/) (2020)
- [Learning from Data with Heterogeneous Noise using SGD](https://mlsanity.com/paper/1412.5617/) (2014)
- [Privacy-aware identification](https://mlsanity.com/paper/2006.14732/) (2020)
- [Center-specific causal inference with multicenter trials: reinterpreting trial evidence in the context of each participating center](https://mlsanity.com/paper/2104.05905/) (2021)

## Built on by (most cited first, 4 of 4)

- [Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis](https://mlsanity.com/paper/2410.16870/) (2024)
- [Model Agnostic Differentially Private Causal Inference](https://mlsanity.com/paper/2505.19589/) (2025)
- [A Survey on Federated Causal Discovery and Inference](https://mlsanity.com/paper/2606.23741/) (2026)
- [Differentially Private Average Treatment Effect Estimation by Propensity Score Blocking](https://mlsanity.com/paper/2609.09536/) (2026)

## Abstract (arXiv)

Patient privacy is a major barrier to healthcare AI. For confidentiality reasons, most patient data remains in silo in separate hospitals, preventing the design of data-driven healthcare AI systems that need large volumes of patient data to make effective decisions. A solution to this is collective learning across multiple sites through federated learning with differential privacy. However, literature in this space typically focuses on differentially private statistical estimation and machine learning, which is different from the causal inference-related problems that arise in healthcare. In this work, we take a fresh look at federated learning with a focus on causal inference; specifically, we look at estimating the average treatment effect (ATE), an important task in causal inference for healthcare applications, and provide a federated analytics approach to enable ATE estimation across multiple sites along with differential privacy (DP) guarantees at each site. The main challenge comes from site heterogeneity -- different sites have different sample sizes and privacy budgets. We address this through a class of per-site estimation algorithms that reports the ATE estimate and its variance as a quality measure, and an aggregation algorithm on the server side that minimizes the overall variance of the final ATE estimate. Our experiments on real and synthetic data show that our method reliably aggregates private statistics across sites and provides better privacy-utility tradeoff under site heterogeneity than baselines.