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原文摘要

Constraint-based causal discovery requires many conditional independence (CI) tests. Generative CI tests are attractive in nonlinear and high-dimensional settings because they can model complex conditional distributions, but often suffer from high per-test costs and limited reusability across query-dependent conditional resampling tasks in graph search. We propose a generative nonlinear CI testing and graph search framework for PC-style causal discovery. We introduce Langevin-RePaint for conditional imputation. Building on Langevin-RePaint and joint flow matching modeling, we propose the Flow Matching based Conditional Independence Test (FMCIT), which reformulates generative CI testing from query-specific conditional modeling into a reusable conditional resampling framework. We establish theoretical guarantees for FMCIT's type-I error control. FMCIT reuses a single trained generative model to generate randomized copies throughout graph search. We then introduce GPC-FMCIT, which embeds FMCIT into a guided PC pipeline with screening, guidance, refinement, and orientation. By using low-cost screening to shrink the search space, constructing edge-specific candidate conditioning sets from local graph structure, and invoking FMCIT only in a budget-controlled refinement stage, GPC-FMCIT reduces both the deployment cost of individual nonlinear CI queries and the overall query complexity. On synthetic nonlinear structural equation models, GPC-FMCIT improves CPDAG recovery over PC-stable baselines and requires far fewer FMCIT queries than direct FMCIT-based PC search, and on hESC gene-expression data it yields smaller graphs than PC/FisherZ in a descriptive comparison. Overall, our results suggest that, through the joint design of reusable conditional resampling and controlled graph search, generative nonlinear CI testing can become a practical component of PC-style causal discovery.