Sunday, October 30, 2011

Thinking about causality

A lot of research, especially evaluation research, seems to infer causality from correlation. Of course we know that there are problems intrinsic to such inferences:

  • correlation may be spurious, occurring as an accidental confluence of different variables
  • two variable may be causally related by the first causing the second, the second causing the first, or each causing the other
  • two variables may be correlated because some third variable causes both.
If one thing is seen as causing another, we may ask if it is a necessary cause, a sufficient cause, or both necessary and sufficient.

Of course, there are many other forms of causality. Think of a situation in which there are five causal factors, any three of which are sufficient but no two of which are sufficient. Or think of a large number of workers, each loading a container at his/her own rate. The container is filled when the total of all the contributions adds to the capacity of the container.

And/Or logic allows for very complicated causal structures, even when each factor is binary. An "and gate" provides a positive output if and only if all of its inputs are positive. An "or" gate provides a positive output if any of its inputs are positive. Very complicated propositions can be implemented through complex structures of such gates. Moreover, these are very restrictive kinds of binary logic concepts.

It seems to me that many evaluations assume that an observed correlation is not only demonstrative of causality, but of a specific form of causality. Let me give an  example. I recall a project in which an industrial extension program was used to improve the rate and success of industrial innovation. In one country it appeared to be very successful, with a large portion of the enterprises receiving assistance reporting success in their innovations. On the other hand, in another country the same approach to industrial extension proved very unsuccessful. It seemed that there were "hidden variables" such as the overall industrial climate. If the industrial climate was positive, an extension service would work, if not the extension program would not work.

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