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In neuroscience, hierarchical models of brain connectivity, particularly in the prefrontal cortex (PFC), are used to understand how the brain can process sensory information, make decisions and perform other high level tasks. Despite extensive research, understanding the structure of the PFC remains a crucial challenge. To this end, we propose a data-driven approach to studying brain signals based on Gaussian processes and causal strengths. For discovering causations, we propose a metric referred to as double-averaged differential causal effect. The differential causal effect has been proposed recently, and it can be used to quantify causal strengths in a principled way. We studied real multivariate time series data that represent local field potentials from the frontal lobe. The interest was in finding the causal relationship between the medial and lateral PFC areas of the brain. Our results suggest that the medial PFC causally influences the lateral PFC.