^ beakman, your point is valid if you have the option of running an experiment. But there are plenty of social science "treatments" that can't actually be randomized. In those situations, matching techniques + OLS are definitely better than just OLS.

Not sure what you mean by better here. Matching techniques and OLS draw on the same information (measured variables) and suffer from the same limitation (unmeasured variables). You might mean that matching techniques make some of the assumptions more transparent than a standard regression model, but it's not as if a matching estimator somehow makes more of the available information than is possible with a standard regression model.

Have you read Gary King's work? He has a different take on matching than you.

Yes ... I've read quite a bit of King's work on matching (and other statistical topics). I'm pretty certain he does not have a different take. Can you point to some statement of his where he claims that a matching estimator gets more out of the data than a standard regression? I recall that he makes the case, as do many other statisticians and econometricians, that matching estimators make assumptions more transparent (e.g., whether covariate distributions are balanced, regions of common support, ...), but I would be quite surprised if he argues for more than this. Alternatively, perhaps you can try to make the case yourself.