Chris Blattman writes:
Matching is not an identification strategy a solution to your endogeneity problem; it is a weighting scheme. Saying matching will reduce endogeneity bias is like saying that the best way to get thin is to weigh yourself in kilos. The statement makes no sense. It confuses technique with substance. . . . When you run a regression, you control for the X you can observe. When you match, you are simply matching based on those same X. . . .
I see what Chris is getting at–matching, like regression, won’t help for the variables you’re not controlling for–but I disagree with his characterization of matching as a weighting scheme. I see matching as a way to restrict your analysis to comparable cases. The statistical motivation: robustness. If you had a good enough model, you wouldn’t neet to match, you’d just fit the model to the data. But in common practice we often use simple regression models and so it can be helpful to do some matching first before regression. It’s not so difficult to match on dozens of variables, but it’s not so easy to include dozens of variables in your least squares regression. So in practice it’s not always the case that “you are simply matching based on those same X. To put it another way: yes, you’ll often need to worry about potential X variables that you don’t have–but that shouldn’t stop you from controlling for everything that you do have, and matching can be a helpful tool in that effort.