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Matching methods for estimating treatment effects using Stata

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  • Guido W. Imbens

    ()
    (Harvard University)

Abstract

I will give a brief overview of modern statistical methods for estimating treatment effects that have recently become popular in social and biomedical sciences. These methods are based on the potential outcome framework developed by Donald Rubin. The specific methods discussed include regression methods, matching, and methods involving the propensity score. I will discuss the assumptions underlying these methods and the methods for assessing their plausability. I will then discuss using the Stata command nnmatch to estimate average treatment effects. I will illustrate this approach by using data from a job training program. A general survey of these methods can be found in Imbens, G. 2004. Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and Statistics 86: 4–30.

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Paper provided by Stata Users Group in its series North American Stata Users' Group Meetings 2006 with number 13.

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Date of creation: 23 Jul 2006
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Handle: RePEc:boc:asug06:13

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