# Differentially Private Covariate Balancing Causal Inference

arXiv: https://arxiv.org/abs/2410.14789 · HTML: https://mlsanity.com/paper/2410.14789/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 47 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Builds on (most cited first, 22 of 22)

- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Balancing Covariates via Propensity Score Weighting](https://mlsanity.com/paper/1609.07494/) (2016)
- [Covariate Balancing Propensity Score by Tailored Loss Functions](https://mlsanity.com/paper/1601.05890/) (2016)
- [Parametric Bootstrap for Differentially Private Confidence Intervals](https://mlsanity.com/paper/2006.07749/) (2020)
- [Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy](https://mlsanity.com/paper/2107.02780/) (2021)
- [Differentially Private Estimation of Heterogeneous Causal Effects](https://mlsanity.com/paper/2202.11043/) (2022)
- [Entropy balancing is doubly robust](https://mlsanity.com/paper/1501.03571/) (2015)
- [Private Causal Inference](https://mlsanity.com/paper/1512.05469/) (2015)
- [Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA](https://mlsanity.com/paper/1901.10864/) (2019)
- [Kernel Balancing: A flexible non-parametric weighting procedure for estimating causal effects](https://mlsanity.com/paper/1605.00155/) (2016)
- [Structure and Sensitivity in Differential Privacy: Comparing K-Norm Mechanisms](https://mlsanity.com/paper/1801.09236/) (2018)
- [Privacy-Preserving Causal Inference via Inverse Probability Weighting](https://mlsanity.com/paper/1905.12592/) (2019)
- [Energy Balancing of Covariate Distributions](https://mlsanity.com/paper/2004.13962/) (2020)
- [Covariate balancing using the integral probability metric for causal inference](https://mlsanity.com/paper/2305.13715/) (2023)
- [KNG: The K-Norm Gradient Mechanism](https://mlsanity.com/paper/1905.09436/) (2019)
- [Differentially Private Model Selection with Penalized and Constrained Likelihood](https://mlsanity.com/paper/1607.04204/) (2016)
- [Locally Private Causal Inference for Randomized Experiments](https://mlsanity.com/paper/2301.01616/) (2023)
- [Optimal Covariate Balancing Conditions in Propensity Score Estimation](https://mlsanity.com/paper/2108.01255/) (2021)
- [Differentially Private Estimation of Weighted Average Treatment Effects for Binary Outcomes](https://mlsanity.com/paper/2408.14766/) (2024)
- [Privacy-Aware Rejection Sampling](https://mlsanity.com/paper/2108.00965/) (2021)
- [Privacy-aware identification](https://mlsanity.com/paper/2006.14732/) (2020)
- [Federated Experiment Design under Distributed Differential Privacy](https://mlsanity.com/paper/2311.04375/) (2023)

## Built on by (most cited first, 5 of 5)

- [Model Agnostic Differentially Private Causal Inference](https://mlsanity.com/paper/2505.19589/) (2025)
- [PrivATE: Differentially Private Confidence Intervals for Average Treatment Effects](https://mlsanity.com/paper/2505.21641/) (2025)
- [Differentially Private Two-Stage Empirical Risk Minimization with Applications to Individualized Treatment Rule](https://mlsanity.com/paper/2602.12604/) (2026)
- [Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration](https://mlsanity.com/paper/2607.08122/) (2026)
- [Differentially Private Average Treatment Effect Estimation by Propensity Score Blocking](https://mlsanity.com/paper/2609.09536/) (2026)

## Abstract (arXiv)

Differential privacy is the leading mathematical framework for privacy protection, providing a probabilistic guarantee that safeguards individuals' private information when publishing statistics from a dataset. This guarantee is achieved by applying a randomized algorithm to the original data, which introduces unique challenges in data analysis by distorting inherent patterns. In particular, causal inference using observational data in privacy-sensitive contexts is challenging because it requires covariate balance between treatment groups, yet checking the true covariates is prohibited to prevent leakage of sensitive information. In this article, we present a differentially private two-stage covariate balancing weighting estimator to infer causal effects from observational data. Our algorithm produces both point and interval estimators with statistical guarantees, such as consistency and rate optimality, under a given privacy budget.