Non-negative DAG Learning from Time-Series Data
It addresses the challenge of non-convex optimization in DAG learning for time-series analysis, offering a more reliable solution for researchers in causal inference and time-series modeling.
This work tackles the problem of learning directed acyclic graphs (DAGs) from multivariate time-series data to capture instantaneous dependencies, by proposing a convex optimization method that assumes non-negative edge weights and guarantees global optimality, outperforming existing alternatives on synthetic data.
This work aims to learn the directed acyclic graph (DAG) that captures the instantaneous dependencies underlying a multivariate time series. The observed data follow a linear structural vector autoregressive model (SVARM) with both instantaneous and time-lagged dependencies, where the instantaneous structure is modeled by a DAG to reflect potential causal relationships. While recent continuous relaxation approaches impose acyclicity through smooth constraint functions involving powers of the adjacency matrix, they lead to non-convex optimization problems that are challenging to solve. In contrast, we assume that the underlying DAG has only non-negative edge weights, and leverage this additional structure to impose acyclicity via a convex constraint. This enables us to cast the problem of non-negative DAG recovery from multivariate time-series data as a convex optimization problem in abstract form, which we solve using the method of multipliers. Crucially, the convex formulation guarantees global optimality of the solution. Finally, we assess the performance of the proposed method on synthetic time-series data, where it outperforms existing alternatives.