Existing causal discovery methods typically require the data to be available in a centralized location. However, many practical domains, such as healthcare, limit access to the data gathered by local entities, primarily for privacy and regulatory constraints. To address this, we propose FED-CD, a federated framework for inferring causal structures from distributed datasets containing observational and interventional data. By exchanging updates instead of data samples, FED-CD ensures privacy while enabling decentralized discovery of the underlying directed acyclic graph (DAG). We accommodate scenarios with shared or disjoint intervened covariates, and mitigate the adverse effects of interventional data heterogeneity. We provide empirical evidence for the performance and scalability of FED-CD for decentralized causal discovery using synthetic and real-world DAGs.

翻译：现有因果发现方法通常要求数据集中在单一位置。然而，在医疗健康等许多实际领域中，由于隐私和监管约束，本地实体收集的数据访问受到限制。为解决这一问题，我们提出FED-CD，一个从包含观测数据和干预数据的分布式数据集中推断因果结构的联邦框架。通过交换更新而非数据样本，FED-CD在确保隐私的同时，实现了对底层有向无环图（DAG）的分布式发现。我们处理了干预协变量共享或不相交的场景，并缓解了干预数据异质性带来的负面影响。我们利用合成和真实DAG，为FED-CD在分布式因果发现中的性能和可扩展性提供了实证证据。