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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