Causal Mediation Analysis Using Weighting Approach
Overview
We implement causal mediation analysis using the methods proposed by Hong (2010) and Hong, Deutsch & Hill (2015) doi:10.3102/1076998615583902. It allows the estimation and hypothesis testing of causal mediation effects through ratio of mediator probability weights (RMPW). This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. We also implement a sensitivity analysis by extending the RMPW method to assess potential bias in the presence of omitted pretreatment or posttreatment covariates. The sensitivity analysis strategy was proposed by Hong, Qin, and Yang (2018) doi:10.3102/1076998617749561.
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- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
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- Help pages
- 4
- Examples that run
- 0%3 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 75%
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- 0.0.7Latest
- RR 4.6.0 released · 2026-04-24
- 0.0.62025-11-17 · diff ↗
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 0.0.52024-03-06 · diff ↗
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- 0.0.42018-07-18 · diff ↗
- RR 3.5.0 released · 2018-04-23
- 0.0.32018-01-04 · diff ↗
- 0.0.2
- RR 3.4.0 released · 2017-04-21
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- 0.0.12017-02-27
- RR 3.3.0 released · 2016-05-03
Package metadata
- First published
- 2017-02-27
- Total releases
- 7 / 9 yrs
- License
- GPL-2 OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 17 KB / 1 file
- Download size
- 33 KB
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