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GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints
Mohammadsajad Abavisani · David Danks · Vince Calhoun · Sergey Plis
Event URL: https://openreview.net/forum?id=qjdr2QiBIWR »

Graphical structures estimated by causal learning algorithms from time series data can provide highly 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 a) require that the difference between causal and measurement timescales is known; b) only handle very small number of random variables when the timescale difference is unknown; c) only apply to pairs of variables (albeit with fewer assumptions about prior knowledge); or d) be unable to find a solution given statistical noise in the data. This paper addresses all four challenges. Our algorithm combines constraint programming with both theoretical insights into the problem structure and prior information about admissible causal interactions to achieve gains of multiple orders of magnitude in speed and informativeness. The resulting system scales to significantly larger sets of random variables (>100) without knowledge of the timescale difference while maintaining theoretical guarantees. This method is also robust to edge misidentification and can use parametric connection strengths, while optionally finding the optimal among many possible solutions.

Author Information

Mohammadsajad Abavisani (Georgia Institute of Technology)
Mohammadsajad Abavisani

I am a third year Ph.D. student in Electrical and Computer Engineering Department of Georgia Institute of Technology. My area of research is Machine Learning, Deep Learning and Causal Learning I am a graduate research assistant in (TReNDS). My areas of interest are developing novel machine learning methods to extract causal relations among complex and big scale data (including fMRI), Self-Supervised Learning and Computer Vision.

David Danks (Carnegie Mellon University)
Vince Calhoun (Georgia State University)
Sergey Plis (TReNDS center, GSU)

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