Inexact Matching using Minimized Distance Metrics
Primer on nearest neighbor matching and robust standard errors with concrete examples of calculations
Matching estimation is possibly the most intuitive causal method outside of the RCT. The idea that if we want to estimate a causal effect, comparing people that look alike sort of just feels right to a lot of people. After all, if selection bias is a result of comparing groups that are super different from one another, then won’t comparing groups that aren’t so different fix it? I mean, assuming we all agree on the confounders, then matching seems like a plan.
And yet despite that very compelling argument I just made, my sense in this job is that matching is far far less common than regression, even in selection on observables situations. Many social scientists are either unfamiliar with matching, or if familiar, both have never actually used it and / or will not contemplate using it. Which is itself intriguing given nonparametric matching has fewer assumptions than regression, not more. But I’m not going to try and twist your ar…