Inferring causal relations from multivariate data using Large-Scale Augmented Granger Causality (lsAGC)

Causal inference from high-dimensional and short time-series data is crucial to scientific discovery across diverse fields. Yet, standard approaches frequently fail under these constraints. We propose Large-scale Augmented Granger Causality (lsAGC), integrating dimension reduction, a Granger-based predictive framework, and data augmentation, to handle large-scale networks even when T<N. Extensive simulations on synthetic and semi-realistic fMRI data (3-34 nodes, both linear and nonlinear) confirm lsAGC's efficiency in tackling high-dimensional data. Validation on real clinical fMRI data from 40 subjects (118 brain regions) demonstrates superior performance, with lsAGC achieving AUC 0.83 versus 0.50-0.62 for modern baselines including PCMCI, sparse VAR, and deconvolution-based GC. Empirically, lsAGC outperforms baseline methods in multiple benchmarks. For instance, on a 34-node network with only 50 samples, lsAGC maintains an AUROC above 0.70, whereas others fall below 0.60. Moreover, lsAGC is computationally efficient (8.3s vs. hours for 118-region networks) and robust to noise, nonlinear effects, and short time spans. This combination of speed and accuracy renders lsAGC practical for real-world contexts in neuroscience, climate science, and economics, where short, large-scale time series predominate.