localIV (0.2.1)

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Estimation of Marginal Treatment Effects using Local Instrumental Variables.

https://github.com/xiangzhou09/localIV
http://cran.r-project.org/web/packages/localIV

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 ). 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 ). 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()).

Maintainer: Xiang Zhou
Author(s): Xiang Zhou [aut, cre]

License: GPL (>= 3)

Uses: KernSmooth, mgcv, sampleSelection, plotly

Released 7 months ago.


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