@inproceedings{HyttinenSJ:IJCAI2017,
author = {Antti Hyttinen and Paul Saikko and Matti J\"arvisalo},
title = {A Core-Guided Approach to Learning Optimal Causal Graphs},
booktitle = {Proceedings of the 26th International Joint Conference
             on Artificial Intelligence (IJCAI 2017)},
editor = {Carles Sierra},
pages = {645--651},
publisher = {IJCAI},
year = {2017},
}


Abstract:

Discovery of causal relations is an important part of data analysis.
Recent exact Boolean optimization approaches enable tackling very
general search spaces of causal graphs with feedback cycles and
latent confounders, simultaneously obtaining high accuracy by
optimally combining conflicting independence information in sample
data. We propose several domain-specific techniques and integrate
them into a core-guided maximum satisfiability solver, thereby
speeding up current state of the art in exact search for causal
graphs with cycles and latent confounders on simulated and
real-world data