title: Causal Discovery from Subsampled Time Series Data by Constraint Optimization.
abstract: This paper focuses on causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system. Previous work has shown that such subsampling can lead to significant errors about the system's causal structure if not properly taken into account. In this paper, we first consider the search for the system timescale causal structures that correspond to a given measurement timescale structure. We provide a constraint satisfaction procedure whose computational performance is several orders of magnitude better than previous approaches. We then consider finite-sample data as input, and propose the first constraint optimization approach for recovering the system timescale causal structure. This algorithm optimally recovers from possible conflicts due to statistical errors. More generally, these advances allow for a robust and non-parametric estimation of system timescale causal structures from subsampled time series data.
authors.affiliation: HIIT, Department of Computer Science, University of Helsinki.
authors.forename: Antti
authors.initials: A
authors.lastname: Hyttinen
authors.affiliation: Mind Research Network and University of New Mexico.
authors.forename: Sergey
authors.initials: S
authors.lastname: Plis
authors.affiliation: HIIT, Department of Computer Science, University of Helsinki.
authors.forename: Matti
authors.initials: M
authors.lastname: Järvisalo
authors.affiliation: Humanities and Social Sciences, California Institute of Technology.
authors.forename: Frederick
authors.initials: F
authors.lastname: Eberhardt
authors.affiliation: Department of Philosophy, Carnegie Mellon University.
authors.forename: David
authors.initials: D
authors.lastname: Danks
keywords: causal discovery; causality; constraint optimization; constraint satisfaction; graphical models; time series