ace {localIV} | R Documentation |
Estimating Average Causal Effects from a Fitted MTE Model.
Description
ace
estimates Average Causal Effects (ACE) from a fitted MTE model.
The estimand can be average treatment effect (ATE), average treatment effect on the treated (ATT),
average treatment effect on the untreated (ATU), or the Marginal Policy Relevant
Treatment Effect (MPRTE) defined in Zhou and Xie (2019).
Usage
ace(model, estimand = c("ate", "att", "atu", "mprte"), policy = 1)
Arguments
model |
A fitted |
estimand |
Type of estimand: |
policy |
An |
Value
Estimate of ATE, ATT, ATU, or MPRTE
References
Heckman, James J., Sergio Urzua, and Edward Vytlacil. 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity." The Review of Economics and Statistics 88:389-432.
Zhou, Xiang and Yu Xie. 2019. "Marginal Treatment Effects from A Propensity Score Perspective." Journal of Political Economy, 127(6): 3070-3084.
Zhou, Xiang and Yu Xie. 2020. "Heterogeneous Treatment Effects in the Presence of Self-selection: a Propensity Score Perspective." Sociological Methodology.
Examples
mod <- mte(selection = d ~ x + z, outcome = y ~ x,
data = toydata)
ate <- ace(mod, "ate")
att <- ace(mod, "att")
atu <- ace(mod, "atu")
mprte1 <- ace(mod, "mprte")
mprte2 <- ace(mod, "mprte", policy = p)
mprte3 <- ace(mod, "mprte", policy = 1-p)
mprte4 <- ace(mod, "mprte", policy = I(p<0.25))
c(ate, att, atu, mprte1, mprte2, mprte3, mprte4)