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JMLR Workshop Conf Proc. 2016 Aug;52:216-227.

Causal Discovery from Subsampled Time Series Data by Constraint Optimization.

Author information

1
HIIT, Department of Computer Science, University of Helsinki.
2
Mind Research Network and University of New Mexico.
3
Humanities and Social Sciences, California Institute of Technology.
4
Department of Philosophy, Carnegie Mellon University.

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.

KEYWORDS:

causal discovery; causality; constraint optimization; constraint satisfaction; graphical models; time series

PMID:
28203316
PMCID:
PMC5305170

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