Federated Causal

Federated causal discovery (FCD) addresses the challenge of learning causal relationships from decentralized data while preserving privacy. Current research focuses on developing algorithms that can handle heterogeneous data distributions across multiple sources, often employing federated conditional independence tests or gradient-based methods to infer causal structures without directly sharing raw data. These advancements are significant for enabling causal inference in sensitive domains like healthcare and finance, where data sharing is restricted, and for improving the robustness and fairness of federated learning models.

Papers

October 22, 2024

Federated Causal Inference: Multi-Centric ATE Estimation beyond Meta-Analysis
Rémi Khellaf, Aurélien Bellet, Julie Josse
Meta Analysis Average Treatment Effect Treatment Assignment Multivariate Probability Distribution Federated Causal

February 20, 2024

Federated Causal Discovery from Heterogeneous Data
Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang
Causal Discovery Causal Structure Heterogeneous Data Causal Discovery Method Federated Causal

December 9, 2023

Federated Causality Learning with Explainable Adaptive Optimization
Dezhi Yang, Xintong He, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Jinglin Zhang
Causal Discovery Causal Graph Data Driven Optimization Federated Causal Causal Learning Algorithm

August 24, 2023

Federated Causal Inference from Observational Data
Thanh Vinh Vo, Young lee, Tze-Yun Leong
Causal Inference Causal Effect Observational Data Federated Causal

June 15, 2023

Towards Practical Federated Causal Structure Learning
Zhaoyu Wang, Pingchuan Ma, Shuai Wang
Causal Graph Causal Structure Causal Relation Causal Structure Learning Federated Causal

February 9, 2023

Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models
Yingchun Wang, Jingcai Guo, Jie Zhang, Song Guo, Weizhan Zhang, Qinghua Zheng
Heterogeneous Environment Edge Model Federated Causal Edge Function Edge Data

November 7, 2022

Federated Causal Discovery From Interventions
Amin Abyaneh, Nino Scherrer, Patrick Schwab, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou
Causal Discovery Causal Structure Heterogeneous Treatment Effect Causal Discovery Method Health Intervention Federated Causal

September 26, 2022

An Energy Optimized Specializing DAG Federated Learning based on Event Triggered Communication
Xiaofeng Xue, Haokun Mao, Qiong Li, Furong Huang
Timely Communication Event Description Directed Acyclic Graph Energy Policy Research Federated Graph Learning Dag DLT Federated Causal

December 7, 2021

FedDAG: Federated DAG Structure Learning
Erdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard Bondell
Graph Drawing Gradient Descent Gradient Based High Reward DAG Federated Causal