GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints

TL;DR

GRACE-C introduces a scalable, constraint-based causal estimation method that handles unknown timescale differences in time series data, overcoming limitations of existing algorithms and maintaining theoretical guarantees.

Contribution

It combines constraint programming with theoretical insights and prior information to efficiently estimate causal structures across larger variable sets without known timescale differences.

Findings

Scales to over 100 variables without timescale knowledge

Robust to edge misidentification and noise

Achieves significant speed-up over previous methods

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.