Learning DAGs and Root Causes from Time-Series Data

Panagiotis Misiakos, Markus Püschel

Keywords: time-series data, root causes, sparsity, structured vector autoregression, directed acyclic graphs

TL;DR: We propose DAG-TFRC, which learns directed acyclic graphs from time-series data generated by only a small number of events (root causes) in nodes and time points.

Abstract: We introduce DAG-TFRC, a novel method for learning directed acyclic graphs (DAGs) from time series with few root causes. By this, we mean that the data are generated by a small number of events at certain, unknown nodes and time points under a structural vector autoregression model. For such data, we (i) learn the DAGs representing both the instantaneous and time-lagged dependencies between nodes, and (ii) discover the location and time of the root causes. For synthetic data with few root causes, DAG-TFRC shows superior performance in accuracy and runtime over prior work, scaling up to thousands of nodes. Experiments on simulated and real-world financial data demonstrate the viability of our sparse root cause assumption. On S\&P 500 data, DAG-TFRC successfully clusters stocks by sectors and discovers major stock movements as root causes.

Primary Area: causal reasoning

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Submission Number: 7942