Description Usage Arguments Details Value References See Also Examples
mte
fits a MTE model using either the semiparametric local instrumental
variables (local IV) method or the normal selection model (Heckman, Urzua, Vytlacil 2006).
The user supplies a formula for the treatment selection equation, a formula for the
outcome equations, and a data frame containing all variables. The function returns an
object of class mte
. Observations that contain NA (either in selection
or
in outcome
) are removed.
1 2 3 4 5 6 7 8 9 10 11  mte(
selection,
outcome,
data = NULL,
method = c("localIV", "normal"),
bw = NULL
)
mte_localIV(mf_s, mf_o, bw = NULL)
mte_normal(mf_s, mf_o)

selection 
A formula representing the treatment selection equation. 
outcome 
A formula representing the outcome equations where the left hand side is the observed outcome and the right hand side includes predictors of both potential outcomes. 
data 
A data frame, list, or environment containing the variables in the model. 
method 
How to estimate the model: either " 
bw 
Bandwidth used for the local polynomial regression in the local IV approach. Default is 0.25. 
mf_s 
A model frame for the treatment selection equations returned by

mf_o 
A model frame for the outcome equations returned by

mte_localIV
estimates \textup{MTE}(x, u) using the semiparametric local IV method,
and mte_normal
estimates \textup{MTE}(x, u) using the normal selection model.
An object of class mte
.
coefs 
A list of coefficient estimates: 
ufun 
A function representing the unobserved component of \textup{MTE}(x, u). 
ps 
Estimated propensity scores. 
ps_model 
The propensity score model, an object of class 
mf_s 
The model frame for the treatment selection equation. 
mf_o 
The model frame for the outcome equations. 
complete_row 
A logical vector indicating whether a row is complete (no missing variables) in the
original 
call 
The matched call. 
Heckman, James J., Sergio Urzua, and Edward Vytlacil. 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity." The Review of Economics and Statistics 88:389432.
mte_at
for evaluating MTE at different values of the latent resistance u;
mte_tilde_at
for evaluating MTE projected onto the propensity score;
ace
for estimating average causal effects from a fitted mte
object.
1 2 3 4 5 6 7 8 9 10 11 12 13  mod < mte(selection = d ~ x + z, outcome = y ~ x, data = toydata, bw = 0.25)
summary(mod$ps_model)
hist(mod$ps)
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)
}

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