For years now I have been in favour of theory-led evaluation approaches. Many of the previous postings on this website are evidence of this. But this post is about something quite different, about a particular form of data mining, how to do it and how it might be useful.
Recently I have started reading about different data mining algorithms, especially the use of what are called classification trees and genetic algorithms (GAs). The latter was the subject of my recent post, about whether we could evolve models of development projects as well as design them. Genetic algorithms are software embodiments of the evolutionary algorithm (i.e. iterated variation, selection, retention) at work in the biological world. They are good for exploring large possibility spaces and for coming up with new solutions that may not be nearby to current practice.
I had wondered if this idea could be connected to the use of Qualitative Comparative Analysis (QCA), a method of identifying configurations of attributes (e.g. of development projects) associated with a particular type of outcomes (e.g. reduced household poverty). QCA is a theory-led approach, which uses very basic forms of data about attributes (i.e. categorical), then describes configurations of these attributes using Boolean logic expressions, and analyses these with the help of software that can compare and manipulate these statements. The aim is to come up with a minimal number of simple “IF…THEN” type statements describing what sorts of conditions are associated with particular outcomes. This is potentially very useful for development aid managers who are often asking about “what works where in what circumstances”. (But before then there is the challenge of getting on top of the technical language required to be able to do QCA).