CITS: Nonparametric Statistical Causal Modeling for High-Resolution Neural Time Series

TL;DR

CITS is a nonparametric framework that infers causal structures from high-resolution neural time series, outperforming existing methods and revealing new functional insights in large-scale brain recordings.

Contribution

Introduces CITS, a novel nonparametric causal inference method for multivariate time series with proven consistency and superior accuracy over existing approaches.

Findings

Uncovered stimulus-specific causal pathways in neural data.

Demonstrated superior accuracy on simulated benchmarks.

Revealed new functional hierarchies in brain recordings.

Abstract

Identifying causal interactions in complex dynamical systems is a fundamental challenge across the computational sciences. Existing functional connectivity methods capture correlations but not causation. While addressing directionality, popular causal inference tools such as Granger causality and the Peter-Clark algorithm rely on restrictive assumptions that limit their applicability to high-resolution time-series data, such as the large-scale recordings now standard in neuroscience. Here, we introduce CITS (Causal Inference in Time Series), a nonparametric framework for inferring statistically causal structure from multivariate time series. CITS models dynamics using a structural causal model of arbitrary Markov order and statistical tests for lagged conditional independence. We prove consistency under mild assumptions and demonstrate superior accuracy over state-of-the-art baselines across simulated linear, nonlinear, and recurrent neural network benchmarks. Applying CITS to large-scale neuronal recordings from the mouse visual cortex, thalamus, and hippocampus, we uncover stimulus-specific causal pathways and inter-regional hierarchies that align with known anatomy while revealing new functional insights. We further highlight CITS ability in accurately identifying conditional dependencies within small inferred neuronal motifs. These results establish CITS as a theoretically grounded and empirically validated method for discovering interpretable statistically causal networks in neural time series. Beyond neuroscience, the framework is broadly applicable to causal discovery in complex temporal systems across domains.