Improving Accuracy and Scalability of the PC Algorithm by Maximizing
  P-value
  A number of attempts have been made to improve accuracy and/or scalability of
the PC (Peter and Clark) algorithm, some well known (Buhlmann, et al., 2010;
Kalisch and Buhlmann, 2007; 2008; Zhang, 2012, to give some examples). We add
here one more tool to the toolbox: the simple observation that if one is forced
to choose between a variety of possible conditioning sets for a pair of
variables, one should choose the one with the highest p-value. One can use the
CPC (Conservative PC, Ramsey et al., 2012) algorithm as a guide to possible
sepsets for a pair of variables. However, whereas CPC uses a voting rule to
classify colliders versus noncolliders, our proposed algorithm, PC-Max, picks
the conditioning set with the highest p-value, so that there are no
ambiguities. We combine this with two other optimizations: (a) avoiding
bidirected edges in the orientation of colliders, and (b) parallelization. For
(b) we borrow ideas from the PC-Stable algorithm (Colombo and Maathuis, 2014).
The result is an algorithm that scales quite well both in terms of accuracy and
time, with no risk of bidirected edges.
