#  localIV: Estimation of Marginal Treatment Effects using Local Instrumental Variables 

 



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*](https://www.mitpressjournals.org/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*](/publications/heterogeneous-treatment-effects-presence-self-selection-propensity-score)). 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()). Available at [*https://cran.r-project.org/web/packages/localIV/index.html*](https://cran.r-project.org/web/packages/localIV/index.html)



 



 

 See also:- [ Software ](/page-categories/software)