 
Summary: An Anytime Algorithm for Causal Inference
Peter Spirtes
Department of Philosophy
Carnegie Mellon University
Pittsburgh, PA 15213
ps7z@andrew.cmu.edu
Abstract
The Fast Casual Inference (FCI) algorithm
searches for features common to observationally
equivalent sets of causal directed acyclic graphs.
It is correct in the large sample limit with
probability one even if there is a possibility of
hidden variables and selection bias. In the worst
case, the number of conditional independence
tests performed by the algorithm grows
exponentially with the number of variables in the
data set. This affects both the speed of the
algorithm and the accuracy of the algorithm on
small samples, because tests of independence
conditional on large numbers of variables have
