Causal Inference for Unobservable Multivariate Outcomes, with Applications to Brain Effective Connectivity

2026-03-31

AI Summary

This study addresses the challenge of evaluating causal effects on multivariate, latent, and interdependent outcomes—such as brain effective connectivity—by proposing a two-stage causal inference framework. First, effective connectivity is estimated from high-dimensional neuroimaging time series; then, inverse probability weighting combined with multiple testing correction is employed to assess the causal impact of external interventions. The method innovatively incorporates sample splitting to mitigate bias arising from internal dependencies among outcomes and integrates causal modeling with rigorous control of multiple hypothesis testing. Theoretical analysis and simulations demonstrate that the approach achieves asymptotic validity while effectively controlling both Type I error and family-wise error rates. Application to ADNI data successfully uncovers the causal influence of amyloid burden on brain effective connectivity.

Abstract

Evaluating the causal effect of an intervention on multivariate outcomes is challenging when the outcomes are interdependent and derived rather than directly observed. Effective connectivity, which summarizes the directional neural communication between brain regions, is one such derived relational outcome. Estimating how external interventions affect effective connectivity introduces two layers of causal inference problems: identifying directional relationships among brain regions from high-dimensional neuroimaging time series and estimating the causal effect of the intervention on these derived relationships. Each layer introduces distinct biases. The first arises from within-outcome dependencies unrelated to the intervention; to address this, we propose a sample-splitting method for estimating meaningful, and potentially causally informative, effective connectivity measures. The second arises from confounding between the intervention and the derived outcomes; to address this, we apply inverse probability weighting methods and incorporate multiple testing when causal effects on multiple components of the outcomes are of interest. We demonstrate, through theoretical results and simulations, that the proposed methods are asymptotically valid under certain conditions with effective type-I and familywise error control. Finally, we apply the proposed methods to examine the causal effect of amyloid on effective connectivity using the resting-state fMRI data from the Alzheimer's Disease Neuroimaging Initiative database.

Problem

• causal inference
• multivariate outcomes
• effective connectivity
• unobservable outcomes
• neuroimaging

Innovation

• causal inference
• effective connectivity
• sample splitting
• inverse probability weighting
• multivariate outcomes