  A trend in all scientific disciplines, based on advances in technology, is
the increasing availability of high dimensional data in which are buried
important information. A current urgent challenge to statisticians is to
develop effective methods of finding the useful information from the vast
amounts of messy and noisy data available, most of which are noninformative.
This paper presents a general computer intensive approach, based on a method
pioneered by Lo and Zheng for detecting which, of many potential explanatory
variables, have an influence on a dependent variable $Y$. This approach is
suited to detect influential variables, 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, by
dealing with many randomly selected small subsets from which smaller subsets
are selected, 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 appropriate analysis. In a sense,
we are confining our attention to locating a few needles in a haystack.
