Title: Estimation of Marginal Treatment Effects using Local
Description: 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
<doi:10.1162/rest.88.3.389>). Given a treatment selection model and an outcome model, the function mte()
estimates the MTE via local instrumental variables (or via a normal selection model) and also the
projection of MTE onto the 2-dimensional space of the propensity score and a latent variable representing
unobserved resistance to treatment (Zhou and Xie 2018 <https://scholar.harvard.edu/files/xzhou/files/zhou-xie_mte2.pdf>).
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()).
Imports: KernSmooth (>= 2.5.0), mgcv (>= 1.8-19), sampleSelection (>=

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
<https://www.journals.uchicago.edu/doi/abs/10.1086/702172>). The object returned by mte() can be used to
estimate conventional parameters such as ATE, ATT, and ATU (via average()) or marginal policy-relevant

estimate conventional parameters such as ATE and ATT (via average()) or marginal policy-relevant

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