GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints
Mohammadsajad Abavisani, Danks, David, Sergey Plis · PubMed · 2022
) be unable to find a solution given statistical noise in the data. This paper addresses these challenges. Our approach combines constraint programming with both theoretical insights into the problem structure and prior information about admissible causal interactions to achieve multiple orders of magnitude in speed-up. The resulting system maintains theoretical guarantees while scaling to significantly larger sets of random variables (> 100) without knowledge of timescale differences. This method is also robust to edge misidentification and can use parametric connection strengths, while optionally finding the optimal solution among many possible ones.