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Implementing matching estimators for average treatment effects in Stata AgEcon
Abadie, Alberto; Drukker, David M.; Herr, Jane Leber; Imbens, Guido W..
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.
Tipo: Journal Article Palavras-chave: Nnmatch; Average treatment effects; Matching; Exogeneity; Unconfoundedness; Ignorability; Research Methods/ Statistical Methods.
Ano: 2004 URL: http://purl.umn.edu/116250
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The Analysis of Randomized Experiments with Missing Data AgEcon
Imbens, Guido W.; Pizer, William A..
The otherwise straightforward analysis of randomized experiments is often complicated by the presence of missing data. In such situations it is necessary to make assumptions about the dependence of the selection mechanism on treatment, response, and covariates. The widely used approach of assuming that the data is missing at random conditional on treatment and other fully observed covariates is shown to be inadequate to describe data from a randomized experiment when partially observed covariates are also present. This paper presents an alternative to the missing at random model (MAR) which is both consistent with the data and preserves the appeal of MAR. In particular, the proposed family of models minimize the discrepancy with MAR while explaining...
Tipo: Working or Discussion Paper Palavras-chave: Research and Development/Tech Change/Emerging Technologies.
Ano: 2000 URL: http://purl.umn.edu/10596
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