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Rick Davies just wrote an interesting post which combined thoughts on QCA (and multi-valued QCA or mvQCA) and classification trees with thoughts on INUS causation and classification trees.
The question was something like: how can we look at a small-to-medium set of cases (like a dozen or a hundred countries or development programs) and tease out which factors are associated with some outcome. In Rick’s example, he looked at some African countries to see which characteristics are associated with a higher percentage of women in parliament.
Over at rpubs.com, I wrote a little post to show an easy way for evaluators to do classification trees using the open-source statistic software R rather than the Rapid Miner and BigML tools which Rick used. The problem I address at the end is how we can be sure if parts of the resulting models are not spurious.
