Towards Practical Federated Causal Structure Learning
This addresses privacy risks in causal structure learning for domains with decentralized data, but it is incremental as it builds on existing federated learning and causal methods.
The paper tackles the problem of learning causal graphs from decentralized data while preserving privacy, proposing FedC2SL, a federated constraint-based method that uses a federated conditional independence test without collecting raw data. It shows encouraging performance and strong resilience to data heterogeneity in evaluations on synthetic and real-world datasets.
Understanding causal relations is vital in scientific discovery. The process of causal structure learning involves identifying causal graphs from observational data to understand such relations. Usually, a central server performs this task, but sharing data with the server poses privacy risks. Federated learning can solve this problem, but existing solutions for federated causal structure learning make unrealistic assumptions about data and lack convergence guarantees. FedC2SL is a federated constraint-based causal structure learning scheme that learns causal graphs using a federated conditional independence test, which examines conditional independence between two variables under a condition set without collecting raw data from clients. FedC2SL requires weaker and more realistic assumptions about data and offers stronger resistance to data variability among clients. FedPC and FedFCI are the two variants of FedC2SL for causal structure learning in causal sufficiency and causal insufficiency, respectively. The study evaluates FedC2SL using both synthetic datasets and real-world data against existing solutions and finds it demonstrates encouraging performance and strong resilience to data heterogeneity among clients.