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Near-Far Matching in R: The nearfar Package

Joseph Rigdon, Michael Baiocchi, Sanjay Basu

Journal of Statistical Software · 2018

doi:10.18637/jss.v086.c05 (opens in a new tab)

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Abstract

Estimating the causal treatment effect of an intervention using observational data is difficult due to unmeasured confounders. Many analysts use instrumental variables (IVs) to introduce a randomizing element to observational data analysis, potentially reducing bias created by unobserved confounders. Several persistent problems in the field have served as limitations to IV analyses, particularly the prevalence of

Abstract licence: CC BY

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