Nonlinear Causal Discovery for Grouped Data
This addresses causal discovery in domains like neuroscience and manufacturing where data is grouped, but it is an incremental extension of existing methods to vector cases.
The authors tackled the problem of inferring cause-effect relationships for groups of variables rather than individual scalars, extending nonlinear additive noise models to random vectors and demonstrating strong performance in simulations and real-world assembly line data.
Inferring cause-effect relationships from observational data has gained significant attention in recent years, but most methods are limited to scalar random variables. In many important domains, including neuroscience, psychology, social science, and industrial manufacturing, the causal units of interest are groups of variables rather than individual scalar measurements. Motivated by these applications, we extend nonlinear additive noise models to handle random vectors, establishing a two-step approach for causal graph learning: First, infer the causal order among random vectors. Second, perform model selection to identify the best graph consistent with this order. We introduce effective and novel solutions for both steps in the vector case, demonstrating strong performance in simulations. Finally, we apply our method to real-world assembly line data with partial knowledge of causal ordering among variable groups.