Causal Inference under Interference with Learned Exposure Mappings

Research investigates causal inference under interference where exposure mappings are learned rather than directly observed, focusing on environmental applications where transport processes induce these mappings. The study highlights how uncertainty in learned transport processes affects exposure mappings and subsequent spillover inference. It compares mechanistic transport models with operator-learning approaches (PDE, PINO, FNO, GeoPT) using both simulated and real-world environmental data.

This research underscores a critical challenge in causal inference for complex systems where observational data is used to derive relationships, particularly when unobserved processes influence key variables. Understanding how different modeling approaches impact the reliability of causal effect estimates is vital for robust decision-making and policy formulation in areas like environmental regulation, resource management, and public health interventions.

Exposure mappings in causal spillover analyses are often assumed known but frequently need to be learned from data, particularly in environmental contexts.

Forward consideration, not a verified fact.