Privacy-Preserving Causal Inference via Inverse Probability Weighting

2019/05/29 by Si Kai Lee, Luigi Gresele, Lee, Si Kai +5 · 4 citations
Computer Science · Mathematics · Medicine · #Advanced Causal Inference Techniques #Artificial intelligence #Bayesian probability #Causal inference #Computer science #Data mining #Econometrics #FOS: Computer and information sciences #Inference #Inverse #Inverse probability #Inverse probability weighting #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #Marginal structural model #Mathematics #Medicine #Observational study #Posterior probability #Propensity score matching #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics #Weighting #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1905.12592

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2019/05/29 · arxiv created 2019/11/01 · arxiv updated 2019/11/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08

Abstract

The use of inverse probability weighting (IPW) methods to estimate the causal effect of treatments from observational studies is widespread in econometrics, medicine and social sciences. Although these studies often involve sensitive information, thus far there has been no work on privacy-preserving IPW methods. We address this by providing a novel framework for privacy-preserving IPW (PP-IPW) methods. We include a theoretical analysis of the effects of our proposed privatisation procedure on the estimated average treatment effect, and evaluate our PP-IPW framework on synthetic, semi-synthetic and real datasets. The empirical results are consistent with our theoretical findings.

Citations

• Causal Inference Using Potential Outcomes
• A Theorem on the Manipulability of Redundant Serial Kinematic Chains
• An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
• Counterfactual Risk Minimization: Learning from Logged Bandit Feedback
• Estimating causal effects of treatments in randomized and nonrandomized studies.
• The central role of the propensity score in observational studies for causal effects

Cited by

• PrivATE: Differentially Private Average Treatment Effect Estimation for Observational Data
• Differentially Private Tests of Fisher's Sharp Null Hypothesis for Binary Outcomes
• PrivATE: Differentially Private Confidence Intervals for Average Treatment Effects
• Model Agnostic Differentially Private Causal Inference

Related

• Model Agnostic Differentially Private Causal Inference
• PrivATE: Differentially Private Average Treatment Effect Estimation for Observational Data
• Private Causal Inference
• Differentially Private Estimation of Weighted Average Treatment Effects for Binary Outcomes
• Differentially Private Estimation of Heterogeneous Causal Effects