xiangzhou09/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). 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 treatment effect.
Getting started
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Package details |
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| Maintainer | |
| License | GPL (>= 3) |
| Version | 0.3.1 |
| URL | https://github.com/xiangzhou09/localIV |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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