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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0.3.1 by Xiang Zhou, a year ago

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Authors: Xiang Zhou [aut, cre]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports KernSmooth, mgcv, rlang, sampleSelection, stats

Suggests dplyr, ggplot2, tidyr

See at CRAN