One question that often comes up in empirical work concerns the appropriate way to calculate standard errors, and in particular the correct level of clustering. Here is a specific version of the question that someone posed, slightly paraphrased:
This week we're introducing our new series that we decided to call 'Ask Guido.' Guido Imbens has kindly agreed to answer technical questions every so often and we are thrilled. For this first installment, Guido starts by answering a question about standard errors and the appropriate level of clustering in matching.
Of all the impact evaluation methods, the one that consistently (and justifiably) comes last in the methods courses we teach is matching. We de-emphasize this method because it requires the strongest assumptions to yield a valid estimate of causal impact. Most importantly this concerns the assumption of unconfoundedness, namely that selection into treatment can be accurately captured solely as a function of observable covariates in the data.
I received a question this week from Kristen Himelein, a bank colleague who is working on an impact evaluation that will use propensity score matching.