Privacy-Preserving Causal Inference via Inverse Probability Weighting

2019-05-29 · Si Kai Lee, Luigi Gresele, Mijung Park, Krikamol Muandet

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.

📄 PDF Abstract BibTeX arXiv:1905.12592

Code (0)

등록된 구현이 없습니다.

Tasks

Causal Inference EconometricsPrivacy Preserving

Model Agnostic Differentially Private Causal Inference

2025-05-26 · Christiant Lebeda, Mathieu Even, Aurélien Bellet, Julie Josse

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 different…

Causal Inference model Privacy Preserving

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration

2026-07-09 · Amir Asiaee, Kaveh Aryan arxiv

Workload-based differentially private (DP) synthetic data methods privately measure aggregate queries and post-process the noisy answers into synthetic records. Generic workloads can achieve strong distributional fidelit…

Causal Inference

Causal inference for data-driven debugging and decision making in cloud computing

2016-03-04 · Philipp Geiger, Lucian Carata, Bernhard Schoelkopf

Cloud computing involves complex technical and economical systems and interactions. This brings about various challenges, two of which are: (1) debugging and control to optimize the performance of computing systems, with…

Causal Inference Cloud Computing counterfactual Decision Making+1

Federated Causal Inference from Observational Data

2023-08-24 · Thanh Vinh Vo, Young Lee, Tze-Yun Leong

Decentralized data sources are prevalent in real-world applications, posing a formidable challenge for causal inference. These sources cannot be consolidated into a single entity owing to privacy constraints. The presenc…

Causal Inference Federated Learning Gaussian Processes Missing Values+1

Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference

2026-06-02 · Roman Plaud, Alexandre Perez-Lebel, Antoine Saillenfest, Thomas Bonald 외 arxiv

Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation. This disconnect is especially critical in Inverse Probability Weight…