🤖 AI Summary
Existing causal discovery methods often violate process priors in multi-stage settings, yielding counterintuitive results and struggling to scale efficiently to large datasets. To address these limitations, this work proposes a causal discovery framework that integrates stage-specific prior knowledge. The approach first employs a structure-knowledge-guided causal ordering algorithm to infer the causal sequence among variables and construct an initial causal graph. It then introduces an efficient pruning mechanism based on stochastic gated neural networks to eliminate spurious edges. Evaluated across multiple datasets, the proposed method significantly outperforms current state-of-the-art techniques, achieving both high accuracy in recovering the true causal structure and substantial gains in computational efficiency.
📝 Abstract
Causality has become an increasingly important tool for gaining a deeper understanding of complex systems. Among various causal analysis methods, causal discovery, which identifies causal relationships among variables from data, has been widely used to uncover underlying causality in diverse processes. However, while multistage processes are prevalent in many fields, existing causal discovery methods may produce counterintuitive results, given the known process knowledge, and may not be computationally efficient for handling large datasets typical of multistage processes. To address this gap, we propose a novel causal discovery method called Order-based Causal Discovery for Multistage Processes (OCDM). OCDM is designed to infer the causal structure of multistage data while preserving their inherent hierarchical and sequential structure by explicitly incorporating process knowledge into the causal discovery process. Specifically, we propose a structural knowledge-informed order-inferring algorithm that infers the causal order of variables by incorporating information about the stage from which each variable originates, based on an order-based causal discovery framework naturally suited for inherently ordered multistage data. Furthermore, to eliminate spurious edges from the initial causal graph generated based on the inferred causal order, we introduce a novel pruning technique using stochastic gated neural networks, which offers greater computational efficiency compared to existing methods. Through experiments on various datasets, we demonstrate that OCDM effectively infers the causal structure of multistage processes, outperforming existing methods.