Description Usage Arguments Value References Examples
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).
1 |
model |
A fitted |
estimand |
Type of estimand: |
policy |
An |
Estimate of ATE, ATT, ATU, or MPRTE
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.
1 2 3 4 5 6 7 8 9 10 11 | 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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.