Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants

TL;DR

This paper introduces a novel federated causal discovery method that handles both horizontal and vertical data settings by aggregating higher-order cumulants, improving causal graph reconstruction while preserving data privacy.

Contribution

It extends federated causal structure learning to vertical settings and develops a cumulant-based approach for global causal inference.

Findings

Superior performance on synthetic data

Effective causal graph reconstruction

Robust causal strength estimation

Abstract

Federated causal discovery aims to uncover the causal relationships between entities while protecting data privacy, which has significant importance and numerous applications in real-world scenarios. Existing federated causal structure learning methods primarily focus on horizontal federated settings. However, in practical situations, different clients may not necessarily contain data on the same variables. In a single client, the incomplete set of variables can easily lead to spurious causal relationships, thereby affecting the information transmitted to other clients. To address this issue, we comprehensively consider causal structure learning methods under both horizontal and vertical federated settings. We provide the identification theories and methods for learning causal structure in the horizontal and vertical federal setting via higher-order cumulants. Specifically, we first aggregate higher-order cumulant information from all participating clients to construct global cumulant estimates. These global estimates are then used for recursive source identification, ultimately yielding a global causal strength matrix. Our approach not only enables the reconstruction of causal graphs but also facilitates the estimation of causal strength coefficients. Our algorithm demonstrates superior performance in experiments conducted on both synthetic data and real-world data.