Federated Causality Learning with Explainable Adaptive Optimization

TL;DR

This paper introduces FedCausal, a federated learning approach for causal discovery that preserves privacy, handles heterogeneous data, and effectively learns a global causal graph without exposing local data.

Contribution

The paper proposes a novel federated causal discovery method that unifies local and global DAG learning with an interpretable, adaptive optimization objective for heterogeneous data.

Findings

FedCausal effectively handles non-iid data.

It outperforms existing methods on synthetic and real datasets.

The approach maintains data privacy while learning causal structures.

Abstract

Discovering the causality from observational data is a crucial task in various scientific domains. With increasing awareness of privacy, data are not allowed to be exposed, and it is very hard to learn causal graphs from dispersed data, since these data may have different distributions. In this paper, we propose a federated causal discovery strategy (FedCausal) to learn the unified global causal graph from decentralized heterogeneous data. We design a global optimization formula to naturally aggregate the causal graphs from client data and constrain the acyclicity of the global graph without exposing local data. Unlike other federated causal learning algorithms, FedCausal unifies the local and global optimizations into a complete directed acyclic graph (DAG) learning process with a flexible optimization objective. We prove that this optimization objective has a high interpretability and can adaptively handle homogeneous and heterogeneous data. Experimental results on synthetic and real datasets show that FedCausal can effectively deal with non-independently and identically distributed (non-iid) data and has a superior performance.