Double Machine Learning Based Structure Identification from Temporal Data
This addresses a fundamental challenge in fields like finance and bio-medicine by improving causal inference from noisy, correlated temporal data, though it appears incremental as it builds on existing double machine learning techniques.
The paper tackles the problem of identifying causal structures from time-series data in the presence of unknown confounding and cycles, proposing a double machine learning method that asymptotically recovers the true structure and demonstrates superior performance in experiments.
Learning the causes of time-series data is a fundamental task in many applications, spanning from finance to earth sciences or bio-medical applications. Common approaches for this task are based on vector auto-regression, and they do not take into account unknown confounding between potential causes. However, in settings with many potential causes and noisy data, these approaches may be substantially biased. Furthermore, potential causes may be correlated in practical applications or even contain cycles. To address these challenges, we propose a new double machine learning based method for structure identification from temporal data (DR-SIT). We provide theoretical guarantees, showing that our method asymptotically recovers the true underlying causal structure. Our analysis extends to cases where the potential causes have cycles, and they may even be confounded. We further perform extensive experiments to showcase the superior performance of our method. Code: https://github.com/sdi1100041/TMLR_submission_DR_SIT