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.


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install.packages("localIV")

0.3.1 by Xiang Zhou, 4 months ago


https://github.com/xiangzhou09/localIV


Report a bug at https://github.com/xiangzhou09/localIV


Browse source code at https://github.com/cran/localIV


Authors: Xiang Zhou [aut, cre]


Documentation:   PDF Manual  


GPL (>= 3) license


Imports KernSmooth, mgcv, rlang, sampleSelection, stats

Suggests dplyr, ggplot2, tidyr


See at CRAN