This article discusses the poparms command, which implements two semiparametric estimators for multivalued treatment effects discussed in Cattaneo (2010, Journal of Econometrics 155: 138–154). The first is a properly reweighted inverse-probability weighted estimator, and the second is an efficient-influence-function estimator, which can be interpreted as having the double-robust property. Our implementation jointly estimates means and quantiles of the potential-outcome distributions, allowing for multiple, discrete treatment levels. These estimators are then used to estimate a variety of multivalued treatment effects. We discuss pre- and postestimation approaches that can be used in conjunction with our main implementation. We illustrate the program and provide a simulation study assessing the finite-sample performance of the inference procedures.
BussoM., DiNardoJ., and McCraryJ.2013. New evidence on the finite sample properties of propensity score reweighting and matching estimators.http://emlab.berkeley.edu/∼jmccrary/BDM2013.pdf.
2.
CattaneoM. D.2010. Efficient semiparametric estimation of multi-valued treatment effects under ignorability. Journal of Econometrics155: 138–154.
3.
CattaneoM. D., and FarrellM. H.2011. Efficient estimation of the dose–response function under ignorability using subclassification on the covariates. InAdvances in Econometrics: Vol. 27—Missing Data Methods: Cross-Sectional Methods and Applications, ed. DrukkerD. M., 93–127. Bingley, UK: Emerald.
4.
ChenX., HongH., and TarozziA.2004. Semiparametric efficiency in GMM models of nonclassical measurement errors, missing data and treatment effects. Discussion Paper No. 1644, Cowles Foundation. http://cowles.econ.yale.edu/P/cd/d16a/d1644.pdf.
5.
ChenX., HongH., and TarozziA.2008. Semiparametric efficiency in GMM models with auxiliary data. Annals of Statistics36: 808–843.
6.
DrukkerD. M., and WigginsV.2004. Verifying the solution from a nonlinear solver: a case study: comment. American Economic Review94: 397–399.
7.
FirpoS.2007. Efficient semiparametric estimation of quantile treatment effects. Econometrica75: 259–276.
8.
HahnJ.1998. On the role of the propensity score in efficient semiparametric estimation of average treatment effects. Econometrica66: 315–331.
9.
HeckmanJ. J., IchimuraH., and ToddP.1998. Matching as an econometric evaluation estimator. Review of Economic Studies65: 261–294.
10.
HeckmanJ. J., and VytlacilE. J.2007. Econometric evaluation of social programs, part I: Causal models, structural models and econometric policy evaluation. In Handbook of Econometrics, ed. HeckmanJ. J., and LeamerE., vol. 6B, 4779–4874. Amsterdam: Elsevier.
11.
HiranoK., and ImbensG. W.2004. The propensity score with continuous treatments. In Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, ed. GelmanA., and MengX.-L., 73–84. Chichester, UK: Wiley.
12.
HiranoK., ImbensG. W., and RidderG.2003. Efficient estimation of average treatment effects using the estimated propensity score. Econometrica71: 1161–1189.
13.
HollandP. W.1986. Statistics and causal inference. Journal of the American Statistical Association81: 945–960.
14.
HorvitzD. G., and ThompsonD. J.1952. A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association47: 663–685.
15.
ImbensG. W.2000. The role of propensity score in estimating dose–response functions. Biometrika87: 706–710.
16.
ImbensG. W.2004. Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and Statistics86: 4–29.
17.
ImbensG. W., NeweyW., and RidderG.2007. Mean-squared-error calculations for average treatment effects. Working Paper 05.34, Institute of Economic Policy Research.
18.
ImbensG. W., and WooldridgeJ. M.2009. Recent developments in the econometrics of program evaluation. Journal of Economic Literature47: 5–86.
19.
KangJ. D. Y., and SchaferJ. L.2007. Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. Statistical Science22: 523–539.
20.
KhanS., and TamerE.2010. Irregular identification, support conditions, and inverse weight estimation. Econometrica78: 2021–2042.
21.
MillimetD. L., and TchernisR.2009. On the specification of propensity scores, with applications to the analysis of trade policies. Journal of Business and Economic Statistics27: 397–415.
22.
MorganS. L., and WinshipC.2007. Counterfactuals and Causal Inference: Methods and Principles for Social Research.New York: Cambridge University Press.
23.
TsiatisA. A.2006. Semiparametric Theory and Missing Data.New York: Springer.
24.
van der LaanM. J., and RobinsJ. M.2003. Unified Methods for Censored Longitudinal Data and Causality.New York: Springer.
25.
WooldridgeJ. M.2010. Econometric Analysis of Cross Section and Panel Data. 2nd ed. Cambridge, MA: MIT Press.
