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). Given a treatment selection equation and an outcome equation, the function mte() estimates the MTE via the semiparametric local instrumental variables method or the normal selection model. The function mte_at() evaluates MTE at different values of the latent resistance u with a given X = x, and the function mte_tilde_at() evaluates MTE projected onto the estimated propensity score. The function ace() estimates population-level average causal effects such as ATE, ATT, or the marginal policy relevant treatment effect.

Version: 0.3.1
Depends: R (≥ 3.3.0)
Imports: KernSmooth (≥ 2.5.0), mgcv (≥ 1.8-19), rlang (≥ 0.4.4), sampleSelection (≥ 1.2-0), stats
Suggests: dplyr, ggplot2, tidyr
Published: 2020-06-26
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
Materials: README NEWS
CRAN checks: localIV results

Downloads:

Reference manual: localIV.pdf
Package source: localIV_0.3.1.tar.gz
Windows binaries: r-devel: localIV_0.3.1.zip, r-release: localIV_0.3.1.zip, r-oldrel: localIV_0.3.1.zip
macOS binaries: r-release: localIV_0.3.0.tgz, r-oldrel: localIV_0.3.1.tgz
Old sources: localIV archive

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