Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation and Policy Evaluation

Fed-CausalDiff introduces a federated learning framework that enables causal inference and policy evaluation across decentralized data sources by separating global causal mechanisms from local confounders. The approach improves accuracy in treatment effect estimation and policy value calculation while reducing communication overhead, addressing a fundamental limitation of standard federated learning methods that cannot handle interventional scenarios.

Fed-CausalDiff addresses a critical gap in federated learning infrastructure. Traditional federated approaches excel at fitting historical data patterns but fail when interventions change system dynamics—a fundamental requirement for policy evaluation, clinical trials, and economic forecasting. This research demonstrates that causal inference at scale requires architectural innovation beyond standard parameter averaging.

The framework's key contribution lies in its decoupled synchronization mechanism. By separating causal score functions (shared globally) from confounding functions (retained locally), the system preserves privacy while enabling cross-site causal reasoning. This decomposition is technically elegant: clients need only synchronize the mechanisms that transfer across environments, not site-specific artifacts that would corrupt inference.

For enterprise and research applications, the implications are substantial. Organizations managing sensitive data across jurisdictions—healthcare systems, financial institutions, smart grids—have historically faced a choice between collaboration and causal validity. Fed-CausalDiff reduces this tension. The demonstrated improvements in average treatment effect (ATE) estimation directly translate to better decision-making for interventions at scale, whether A/B testing infrastructure, clinical protocols, or resource allocation.

The communication efficiency gains matter equally. Federated systems face bandwidth constraints in real deployments. By limiting synchronization to causal parameters rather than full model states, Fed-CausalDiff reduces network overhead while maintaining fidelity. This creates practical feasibility for deployment in bandwidth-constrained environments like mobile networks or IoT systems. Future work should validate performance across heterogeneous data distributions and adversarial settings, particularly in financial and healthcare domains where causal accuracy directly impacts outcomes.

• →Fed-CausalDiff enables causal inference in federated settings by decoupling global causal mechanisms from local confounders
• →Decoupled synchronization reduces communication costs while improving treatment effect estimation accuracy across distributed nodes
• →The framework addresses a fundamental limitation: standard federated learning cannot handle interventional inference required for policy evaluation
• →Experimental validation on four datasets demonstrates superior ATE and policy-value estimation compared to observational baselines
• →Architecture enables privacy-preserving causal reasoning across sensitive data silos in healthcare, finance, and enterprise applications