Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis
We study Federated Causal Inference, an approach to estimate treatment effects from decentralized data across centers. We compare three classes of Average Treatment Effect (ATE) estimators derived from the Plug-in G-Formula, ranging from simple meta-analysis to one-shot and multi-shot federated learning, the latter leveraging the full data to learn the outcome model (albeit requiring more communication). Focusing on Randomized Controlled Trials (RCTs), we derive the asymptotic variance of these estimators for linear models. Our results provide practical guidance on selecting the appropriate estimator for various scenarios, including heterogeneity in sample sizes, covariate distributions, treatment assignment schemes, and center effects. We validate these findings with a simulation study.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal InferenceFederated LearningSimilar Papers 제목 키워드 기반
Federated Causal Inference in Healthcare: Methods, Challenges, and Applications
Federated causal inference enables multi-site treatment effect estimation without sharing individual-level data, offering a privacy-preserving solution for real-world evidence generation. However, data heterogeneity acro…
Causal InferencePrivacy PreservingExplainable Federated Bayesian Causal Inference and Its Application in Advanced Manufacturing
Causal inference has recently gained notable attention across various fields like biology, healthcare, and environmental science, especially within explainable artificial intelligence (xAI) systems, for uncovering the ca…
Causal InferenceExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)Federated LearningFederated Estimation of Causal Effects from Observational Data
Many modern applications collect data that comes in federated spirit, with data kept locally and undisclosed. Till date, most insight into the causal inference requires data to be stored in a central repository. We prese…
Causal InferenceGaussian ProcessesFederated Causal Inference from Observational Data
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 InferenceFederated LearningGaussian ProcessesMissing Values+1Multiply Robust Federated Estimation of Targeted Average Treatment Effects
Federated or multi-site studies have distinct advantages over single-site studies, including increased generalizability, the ability to study underrepresented populations, and the opportunity to study rare exposures and …
Privacy PreservingTransfer Learningvalid