Matlab functions to run the ICA-based Two-Step algorithm introduced in Sanchez-Romero, R., Ramsey, J. D., Zhang, K., Glymour, M. R., Huang, B., & Glymour, C. (2019). Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods. Network Neuroscience, 3(2), 274-306.
The algorithm was designed and applied to fMRI data but can be applied to other non-Gaussian, linear problems.
Input: a dataset X of non-Gaussian variables, together with a positive value for the regularization parameter, lambda.
Output: matrix B of causal coefficient estimates, from X = BX + E.
In matrix B, the causal direction goes from column to row, such that a matrix entry Bij, implies Xj --> Xi
two_step_CD.m runs Two-Step with Adaptative Lasso as a first step, using lambda to control the regularization.
two_step_CD_mask.m should be used if the adjacency matrix of the first step was computed with some other algorithm.