xiangzhou09/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 local instrumental variables (or via a normal selection model) and also the projection of MTE onto the 2-dimensional space of the propensity score and a latent variable representing unobserved resistance to treatment (Zhou and Xie 2018 <https://scholar.harvard.edu/files/xzhou/files/zhou-xie_mte2.pdf>). 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()).

Getting started

Package details

Maintainer
LicenseGPL (>= 3)
Version0.1.0
URL https://github.com/xiangzhou09/localIV
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("xiangzhou09/localIV")
xiangzhou09/localIV documentation built on Aug. 5, 2018, 12:02 a.m.