Traditionally, Statistics has been concerned with uncovering and describing associations, and statisticians have been wary of causal interpretations of their findings. But users of Statistics have rarely had such qualms. For otherwise what is it all for?
The enterprise of “Statistical Causality” has developed to take such concerns seriously. It has led to the introduction of a variety of formal methods for framing and understanding causal questions, and specific techniques for collecting and analysing data to shed light on these.
One of the examples mentioned is the quite simple but highly unintuitive Simpson's Paradox. The Berkeley sex bias case is particularly bizarre.
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