Estimation of multivalued treatment effects under conditional independence

Matias D. Cattaneo, David M. Drukker, Ashley D. Holland

Research output: Contribution to journalArticlepeer-review

65 Scopus citations

Abstract

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-influencefunction estimator, which can be interpreted as having the double-robust property. Our implementation jointly estimates means and quantiles of the potentialoutcome 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.

Original languageEnglish (US)
Pages (from-to)407-450
Number of pages44
JournalStata Journal
Volume13
Issue number3
DOIs
StatePublished - Sep 2013

All Science Journal Classification (ASJC) codes

  • Mathematics (miscellaneous)

Keywords

  • Bfit
  • Generalized propensity score
  • Inverse-probability weighting
  • Multivalued treatment effects
  • Poparms
  • Semiparametric estimation
  • St0303
  • Treatment effects
  • Unconfoundedness

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