Learning Signals and Graphs from Time-Series Graph Data with Few Causes

Panagiotis Misiakos, Vedran Mihal, Markus Püschel

Abstract: In this paper we port assumptions and techniques from DAG (directed acyclic graph) learning and causal inference to time-series graph data. In particular, we view such data as indexed by a DAG obtained by unrolling the graph in time and generated by a causal linear structural equation model (SEM) from only few causes. For this situation we solve two problems: (1) learning the time series from samples, and (2) learning the graph from time-series data by first learning the entire DAG and then extracting the result. We empirically evaluate our approach targeting the few-causes assumption on both synthetic and real-world data and show significant improvements over prior methods.