Reviews: Selecting causal brain features with a single conditional independence test per feature

Summary: Conditional Independence Testing is an important part of causal structure learning algorithms. However, in the most general case either one has to do a lot of conditional independence tests and/or test by conditioning on a very large number of variables. This work proposes using at most two CI tests per candidate parent involving exactly at most one conditioning variable to filter the real parents of a response variable under certain conditions. This work is interested in identifying direct causes of a Response variable from amongst a set of a candidate parent variables {M_i}. Response variable does not have any observed descendants.