Model Agnostic Differentially Private Causal Inference

TL;DR

This paper introduces a flexible, model-agnostic framework for differentially private causal effect estimation that separates nuisance estimation from privacy mechanisms, enabling the use of advanced models while ensuring privacy.

Contribution

It proposes a novel, general approach for differentially private causal inference that decouples nuisance estimation from privacy, allowing for flexible modeling and improved privacy-utility trade-offs.

Findings

Maintains competitive performance under realistic privacy budgets.

Provides formal utility and privacy guarantees.

Applicable to classical estimators like G-Formula, IPW, and AIPW.

Abstract

Estimating causal effects from observational data is essential in fields such as medicine, economics and social sciences, where privacy concerns are paramount. We propose a general, model-agnostic framework for differentially private estimation of average treatment effects (ATE) that avoids strong structural assumptions on the data-generating process or the models used to estimate propensity scores and conditional outcomes. In contrast to prior work, which enforces differential privacy by directly privatizing these nuisance components, our approach decouples nuisance estimation from privacy protection. This separation allows the use of flexible, state-of-the-art black-box models, while differential privacy is achieved by perturbing only predictions and aggregation steps within a fold-splitting scheme with ensemble techniques. We instantiate the framework for three classical estimators -- the G-Formula, inverse propensity weighting (IPW), and augmented IPW (AIPW) -- and provide formal utility and privacy guarantees, together with privatized confidence intervals. Empirical results on synthetic and real data show that our methods maintain competitive performance under realistic privacy budgets.