mlsanityData to 14 Oct 2026

Differentially Private Covariate Balancing Causal Inference

arXiv 2410.14789 ↗First posted 18 Oct 2024stat.ME cs.CR cs.LGcited by 5

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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.

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

22 of 47 references.

References · 22

  1. 2014RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Responsecited by 272
  2. 2016Balancing Covariates via Propensity Score Weightingcited by 65
  3. 2016Covariate Balancing Propensity Score by Tailored Loss Functionscited by 34
  4. 2020Parametric Bootstrap for Differentially Private Confidence Intervalscited by 19
  5. 2021Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacycited by 18
  6. 2022Differentially Private Estimation of Heterogeneous Causal Effectscited by 14
  7. 2015Entropy balancing is doubly robustcited by 13
  8. 2015Private Causal Inferencecited by 10
  9. 2019Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCAcited by 10
  10. 2016Kernel Balancing: A flexible non-parametric weighting procedure for estimating causal effectscited by 9
  11. 2018Structure and Sensitivity in Differential Privacy: Comparing K-Norm Mechanismscited by 9
  12. 2019Privacy-Preserving Causal Inference via Inverse Probability Weightingcited by 9
10 more
  1. 2020Energy Balancing of Covariate Distributionscited by 9
  2. 2023Covariate balancing using the integral probability metric for causal inferencecited by 9
  3. 2019KNG: The K-Norm Gradient Mechanismcited by 8
  4. 2016Differentially Private Model Selection with Penalized and Constrained Likelihoodcited by 7
  5. 2023Locally Private Causal Inference for Randomized Experimentscited by 7
  6. 2021Optimal Covariate Balancing Conditions in Propensity Score Estimationcited by 6
  7. 2024Differentially Private Estimation of Weighted Average Treatment Effects for Binary Outcomescited by 6
  8. 2021Privacy-Aware Rejection Samplingcited by 5
  9. 2020Privacy-aware identificationcited by 4
  10. 2023Federated Experiment Design under Distributed Differential Privacycited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Model Agnostic Differentially Private Causal Inferencecited by 2
  2. 2025PrivATE: Differentially Private Confidence Intervals for Average Treatment Effectscited by 2
  3. 2026Differentially Private Two-Stage Empirical Risk Minimization with Applications to Individualized Treatment Rulecited by 0
  4. 2026Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibrationcited by 0
  5. 2026Differentially Private Average Treatment Effect Estimation by Propensity Score Blockingcited by 0