We consider a computer intensive approach (Partition Retention (PR), Chernoff, Lo and Zheng (09)), based on an earlier method (Lo and Zheng (2002) for detecting which, of many potential explanatory variables, have an influence on a dependent variable Y. This approach is suited to detect influential variables in groups, where causal effects depend on the confluence of values of several variables. It has the advantage of avoiding a difficult direct analysis, involving possibly thousands of variables, guided by a measure of influence I. The main objective is to discover the influential variables, rather than to measure their effects. Once they are detected, the problem of dealing with a much smaller group of influential variables should be vulnerable to standard analysis. We are confining our attention to locating a few needles in a haystack.