localIV: Estimation of Marginal Treatment Effects using Local Instrumental Variables

In the generalized Roy model, the marginal treatment effect (MTE) can be used as a building block for constructing conventional causal parameters such as the average treatment effect (ATE) and the average treatment effect on the treated (ATT) (Heckman, Urzua, and Vytlacil 2006 <doi:10.1162/rest.88.3.389>). Given a treatment selection model and an outcome model, the function mte() estimates the MTE via a semiparametric local instrumental variables method (or via a normal selection model). The function eval_mte() evaluates MTE at any combination of covariates x and latent resistance u, and the function eval_mte_tilde() evaluates MTE projected onto the estimated propensity score (Zhou and Xie 2019 <https://www.journals.uchicago.edu/doi/abs/10.1086/702172>). The object returned by mte() can be used to estimate conventional parameters such as ATE and ATT (via average()) or marginal policy-relevant treatment effects (via mprte()).

Version: 0.2.1
Depends: R (≥ 3.3.0)
Imports: KernSmooth (≥ 2.5.0), mgcv (≥ 1.8-19), sampleSelection (≥ 1.2-0), stats
Suggests: plotly
Published: 2019-05-06
Author: Xiang Zhou [aut, cre]
Maintainer: Xiang Zhou <xiang_zhou at fas.harvard.edu>
BugReports: https://github.com/xiangzhou09/localIV
License: GPL (≥ 3)
URL: https://github.com/xiangzhou09/localIV
NeedsCompilation: no
CRAN checks: localIV results


Reference manual: localIV.pdf
Package source: localIV_0.2.1.tar.gz
Windows binaries: r-devel: localIV_0.2.1.zip, r-release: localIV_0.2.1.zip, r-oldrel: localIV_0.2.1.zip
OS X binaries: r-release: localIV_0.2.1.tgz, r-oldrel: localIV_0.2.1.tgz
Old sources: localIV archive


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