Causal Discovery with Graph Neural Networks and Intervention Simulation
Abstract
Traditional methods for causal discovery often face challenges when dealing with intricate, high-dimensional datasets, frequently failing to accurately capture complex causal relationships. Furthermore, these methods typically lack the capability to simulate the consequences of interventions, hindering the ability to validate discovered causal structures and estimate their effects. This paper introduces a novel approach that integrates Graph Neural Networks (GNNs) with a dedicated simulation engine. The GNN learns the underlying graph structure from the data, establishing a representation of the causal relationships. Simultaneously, the simulation engine leverages this learned graph to model the impact of interventions – specifically, the addition or removal of edges – ultimately enabling the estimation of causal effects. This synergistic combination provides a robust framework for both discovering causal relationships and validating their accuracy through simulated interventions, addressing the limitations of existing causal discovery techniques. The proposed method offers a pathway to more reliable causal inference in complex systems.