Nonlinear Causal Discovery for Grouped Data

TL;DR

This paper extends nonlinear additive noise models to infer causal relationships among groups of variables, enabling causal discovery in complex domains like neuroscience and manufacturing.

Contribution

It introduces a two-step approach for causal graph learning with vector variables, including novel solutions for order inference and model selection.

Findings

Strong performance demonstrated in simulations

Effective causal order inference among variable groups

Successful application to real-world assembly line data

Abstract

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.