localIV
0.3.1Estimation of Marginal Treatment Effects using Local Instrumental Variables
Overview
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.
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- NOTE2026-06-090 OK · 13 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-080 OK · 12 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-03-100 OK · 14 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 60%
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Nothing depends on this yet.
Code & Tests
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People & History
5 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 0.3.1Latest
- RR 4.0.0 released · 2020-04-24
- 0.3.02020-04-01 · diff ↗
- 0.2.12019-05-06 · diff ↗
- 0.2.02019-04-28 · diff ↗
- RR 3.6.0 released · 2019-04-26
- 0.1.02018-08-05
- RR 3.5.0 released · 2018-04-23
Package metadata
- First published
- 2018-08-05
- Total releases
- 5 / 8 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 3.3.0
- Bundled data
- 230 KB / 1 file
- Download size
- 269 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet