Algorithmic Identification of Essential Exogenous Nodes for Causal Sufficiency in Brain Networks
This work addresses the critical but often overlooked assumption of causal sufficiency in neuroscience, offering a method to reduce errors in causal analysis of brain networks, though it appears incremental as it builds on existing algorithms like PC and NF-iVAE.
The study tackled the problem of ensuring causal sufficiency in brain network analysis by proposing an algorithmic approach to identify essential exogenous nodes, demonstrating that only dorsal regions act as confounders for visual networks in HCP data, with results validated over 30 independent runs.
In the investigation of any causal mechanisms, such as the brain's causal networks, the assumption of causal sufficiency plays a critical role. Notably, neglecting this assumption can result in significant errors, a fact that is often disregarded in the causal analysis of brain networks. In this study, we propose an algorithmic identification approach for determining essential exogenous nodes that satisfy the critical need for causal sufficiency to adhere to it in such inquiries. Our approach consists of three main steps: First, by capturing the essence of the Peter-Clark (PC) algorithm, we conduct independence tests for pairs of regions within a network, as well as for the same pairs conditioned on nodes from other networks. Next, we distinguish candidate confounders by analyzing the differences between the conditional and unconditional results, using the Kolmogorov-Smirnov test. Subsequently, we utilize Non-Factorized identifiable Variational Autoencoders (NF-iVAE) along with the Correlation Coefficient index (CCI) metric to identify the confounding variables within these candidate nodes. Applying our method to the Human Connectome Projects (HCP) movie-watching task data, we demonstrate that while interactions exist between dorsal and ventral regions, only dorsal regions serve as confounders for the visual networks, and vice versa. These findings align consistently with those resulting from the neuroscientific perspective. Finally, we show the reliability of our results by testing 30 independent runs for NF-iVAE initialization.