Doubly Robust Structure Identification from Temporal Data
Emmanouil Angelis Francesco Quinzan Ashkan Soleymani Patrick Jaillet Stefan Bauer
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. Moreover, existing
algorithms often do not work with cyclic data. To address these challenges, we
propose a new doubly robust method for Structure Identification from Temporal
Data ( SITD ). 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 be
confounded. We further perform extensive experiments to showcase the superior
performance of our method.