This paper presents an implementation of matching estimators for average treatment effects in Stata. The nnmatch command allows you to estimate the average effect for all units or only for the treated or control units; to choose the number of matches; to specify the distance metric; to select a bias adjustment; and to use heteroskedastic-robust variance estimators.
AbadieA., and ImbensG.2002. Simple and bias-corrected matching estimators. Technical report, Department of Economics, University of California, Berkeley. http://emlab.berkeley.edu/users/imbens/.
2.
CochranW., and RubinD.B.1973. Controlling bias in observational studies. Sankhyā35: 417–446.
3.
DehejiaR. H., and WahbaS.1999. Causal effects in nonexperimental studies: Reevaluation of the evaluation of training programs. Journal of the American Statistical Association94: 1053–1062.
4.
HeckmanJ., IchimuraH., and ToddP.1998. Matching as an econometric evaluation estimator. Review of Economic Studies65(2): 261–294.
5.
ImbensG.2004. Nonparametric estimation of average treatment effects under exogene-ity: A review. Review of Economics and Statistics86(1): 4–29.
6.
LalondeR. J.1986. Evaluating the econometric evaluations of training programs. American Economic Review76(4): 604–620.
RosenbaumP. R., and RubinD.B.1983. The central role of the propensity score in observational studies for causal effects. Biometrika70(1): 41–55.
9.
RosenbaumP. R.1985. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. American Statistician39: 33–38.
10.
RubinD. B.1973. The use of matched sampling and regression adjustments to remove bias in observational studies. Biometrics29: 185–203.
11.
RubinD. B., and ThomasN.1992. Affinely invariant matching methods with ellipsoidal distributions. Annals of Statistics20: 1079–1093.
12.
WooldridgeJ. M.2002. Econometric Analysis of Cross Section and Panel Data.Cambridge, MA: MIT Press.