@inproceedings{HyttinenHEJ:UAI2013,
author = {Antti Hyttinen and Patrik Hoyer and
       Frederick Eberhardt and Matti J\"arvisalo},
title = {Discovering Cyclic Causal Models with Latent Variables: 
      A General {SAT}-Based Procedure},
booktitle = {Proceedings of the 29th Conference on Uncertainty in 
	  Artificial Intelligence (UAI 2013)},
editor = {Ann Nicholson and Padhraic Smyth},
publisher = {AUAI Press},
pages = {301--310},
year = {2013},
}

Abstract:

We present a very general approach to learning
the structure of causal models based on
d-separation constraints, obtained from any
given set of overlapping passive observational
or experimental data sets. The procedure allows
for both directed cycles (feedback loops)
and the presence of latent variables. Our approach
is based on a logical representation of
causal pathways, which permits the integration
of quite general background knowledge,
and inference is performed using a Boolean
satisability (SAT) solver. The procedure is
complete in that it exhausts the available information
on whether any given edge can be
determined to be present or absent, and returns
\unknown" otherwise. Many existing
constraint-based causal discovery algorithms
can be seen as special cases, tailored to circumstances
in which one or more restricting
assumptions apply. Simulations illustrate the
eect of these assumptions on discovery and
how the present algorithm scales.