Causal-Structure-Preserving Transformation for Root-Cause Discovery

Root-cause discovery (RCD) for a single interventional sample is important in many applications, including rare-disease diagnosis and cloud-service monitoring. Existing methods for one-sample RCD typically assume a linear structural equation model (SEM). In practice, however, observed common causes may affect variables through nonlinear or interaction effects, such as those involving age, gender, and experimental batch in RNA-sequencing studies, or incoming request load in cloud services. Ignoring these common causes can result in unaccounted-for confounding, whereas directly incorporating their nonlinear or interaction effects violates the linearity assumption underlying existing methods. These complications can obscure the sparse intervention signal and cause existing RCD methods to misidentify the root cause. To address this problem, we propose CSP-RCD, a two-stage procedure that first transforms the variables using flexible regression methods and then applies one-sample RCD algorithms to the transformed variables. We prove that this transformation preserves both the causal coefficient matrix and the sparse structural intervention of the original SEM, thereby providing a theoretical basis for the validity of downstream RCD methods. We provide a complete characterization of valid additive transformations and establish consistency of root-cause recovery for CSP-RCD. The proposed transformation can also be applied to other causal discovery tasks. For example, it can be combined with LiNGAM to estimate the causal structure when the variables are influenced by nonlinear effects of observed common causes. Extensive experiments on synthetic causal systems and real-world applications to the PetShop microservice benchmark and an RNA-sequencing dataset show that our proposed approach improves root-cause discovery.