mte_at: Evaluate Marginal Treatment Effects from a Fitted MTE Model.

In xiangzhou09/localIV: Estimation of Marginal Treatment Effects using Local Instrumental Variables

Description

mte_at evaluates marginal treatment effects at different values of the latent resistance u with a given X=x.

Usage

mte_at(x = NULL, u, model)

Arguments

x	Values of the pretreatment covariates at which \textup{MTE}(x, u) is evaluated. It should be a numeric vector whose length is one less than the number of columns of the design matrix X in the outcome model. Default is the sample means.
u	A numeric vector. Values of the latent resistance u at which \textup{MTE}(x, u) is evaluated. Note that the estimation involves extrapolation when the specified u values lie outside of the support of the propensity score.
model	A fitted MTE model returned by mte.

Value

mte_at returns a data frame.

u	input values of u.
x_comp	the x-component of the estimated \textup{MTE}(x, u)
u_comp	the u-component of the estimated \textup{MTE}(x, u)
value	estimated values of \textup{MTE}(x, u)

Examples

mod <- mte(selection = d ~ x + z, outcome = y ~ x, data = toydata)

mte_vals <- mte_at(u = seq(0.05, 0.95, 0.1), model = mod)
if(require("ggplot2")){
  ggplot(mte_vals, aes(x = u, y = value)) +
  geom_line(size = 1) +
  xlab("Latent Resistance U") +
  ylab("Estimates of MTE at Mean Values of X") +
  theme_minimal(base_size = 14)
}

xiangzhou09/localIV documentation built on June 28, 2020, 1:38 a.m.