Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes

arXiv:2402.18477v1 Announce Type: new
Abstract: Inferring the causal structure underlying stochastic dynamical systems from observational data holds great promise in domains ranging from science and health to finance. Such processes can often be accurately modeled via stochastic differential equations (SDEs), which naturally imply causal relationships via "which variables enter the differential of which other variables". In this paper, we develop a kernel-based test of conditional independence (CI) on "path-space" -- solutions to SDEs -- by leveraging recent advances in signature …

abstract arxiv cs.ai cs.lg data differential discovery domains finance health imply kernel processes relationships science stat.ml stochastic systems tests type variables via