GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints
Mohammadsajad Abavisani, David Danks, Sergey Plis
TL;DR
GRACE-C addresses the challenge of inferring causal structure when data are undersampled relative to the underlying dynamics. It reframes rate-agnostic causal discovery as a constraint satisfaction problem using Answer Set Programming, and exploits SCC structure and prior information to dramatically accelerate computation while preserving completeness and correctness. The approach, named sRASL, scales to graphs with over 100 nodes, remains robust to edge misidentification, and supports an optimization mode to handle noisy inputs. The work promises practical impact for domains like neuroscience (e.g., fMRI) where measurement timescales lag behind causal processes, enabling reliable inference under undersampling.
Abstract
Graphical structures estimated by causal learning algorithms from time series data can provide misleading causal information if the causal timescale of the generating process fails to match the measurement timescale of the data. Existing algorithms provide limited resources to respond to this challenge, and so researchers must either use models that they know are likely misleading, or else forego causal learning entirely. Existing methods face up-to-four distinct shortfalls, as they might 1) require that the difference between causal and measurement timescales is known; 2) only handle very small number of random variables when the timescale difference is unknown; 3) only apply to pairs of variables; or 4) be unable to find a solution given statistical noise in the data. This research 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.