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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.3.1 |
rolling linux/jammy R-4.5 | localIV_0.3.1.tar.gz |
300.8 KiB |
0.3.1 |
rolling linux/noble R-4.5 | localIV_0.3.1.tar.gz |
300.7 KiB |
0.3.1 |
rolling source/ R- | localIV_0.3.1.tar.gz |
268.8 KiB |
0.3.1 |
latest linux/jammy R-4.5 | localIV_0.3.1.tar.gz |
300.8 KiB |
0.3.1 |
latest linux/noble R-4.5 | localIV_0.3.1.tar.gz |
300.7 KiB |
0.3.1 |
latest source/ R- | localIV_0.3.1.tar.gz |
268.8 KiB |
0.3.1 |
2026-04-26 source/ R- | localIV_0.3.1.tar.gz |
268.8 KiB |
0.3.1 |
2026-04-23 source/ R- | localIV_0.3.1.tar.gz |
268.8 KiB |
0.3.1 |
2026-04-09 windows/windows R-4.5 | localIV_0.3.1.zip |
305.2 KiB |
0.3.1 |
2025-04-20 source/ R- | localIV_0.3.1.tar.gz |
268.8 KiB |
Dependencies (latest)
Imports
- KernSmooth (>= 2.5.0)
- mgcv (>= 1.8-19)
- rlang (>= 0.4.4)
- sampleSelection (>= 1.2-0)
- stats