Controlled Difference Estimation for Complex Surveys
Overview
Estimates the population average controlled difference for a given outcome between levels of a binary treatment, exposure, or other group membership variable of interest for clustered, stratified survey samples where sample selection depends on the comparison group. Provides three methods for estimation, namely outcome modeling and two factorizations of inverse probability weighting. Under stronger assumptions, these methods estimate the causal population average treatment effect. Salerno et al., (2024) doi:10.48550/arXiv.2406.19597.
- **Added**: support for binary and count outcomes via generalized estimating equations
- **Added**: Augmented inverse probability weighted (AIPW) estimator to main function via
id_form = "DR" - **Improved**: Expanded simulation function to allow for binary and count outcomes, stratified clustered sampling, and increased parameters to specify the true propensity, selection, and outcome models.
- **Updated**: Main function to be faster for larger sample sizes. Separated pieces of estimation workflow into helper functions (helpers.R) for better compartmentalization.
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Documentation
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- Exported functions with examples
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- Documented parameters
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- Return-value docs
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- Help pages citing references
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Package metadata
- First published
- 2024-08-20
- Total releases
- 2 / 2 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 4.1.0
- Bundled data
- 237 KB / 1 file
- Download size
- 4.1 MB
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