@inproceedings{HyttinenPJED:PGM2016,
author = {Antti Hyttinen and Sergey M. Plis and Matti J\"arvisalo and 
       Frederick Eberhardt and David Danks},
title = {Causal Discovery from Subsampled Time Series Data by 
         Constraint Optimization},
editor = {Alessandro Antonucci and
               Giorgio Corani and
               Cassio Polpo de Campos},
booktitle = {Proceedings of the 8th International Conference on 
	     Probabilistic Graphical Models (PGM 2016)},
series = {JMLR Workshop and Conference Proceedings},
volume = {52},
pages = {216--227},
year = {2016},
publisher = {JMLR.org},
}

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.