WeightIt: Balancing Weights for Causal Effect Estimation

The WeightIt package generates balancing weights for causal effect estimation in observational studies. It supports binary, multi-category, or continuous point or longitudinal treatments by extending the functionality of several R packages and providing in-house estimation methods. Methods include parametric modeling, optimization, and machine learning, with tools for weight assessment and weighted regression via M-estimation or bootstrapping.

Use Cases

• Estimate causal effects in observational medical studies based on support for binary, multi-category, or continuous treatments.
• Assess covariate balance and weighting quality based on direct interface with the 'cobalt' package.
• Fit weighted regression models that account for weight uncertainty based on available M-estimation or bootstrapping methods.
• Apply machine learning methods for propensity score estimation based on the description of available estimation techniques.

Strengths

• Supports a range of treatment types including binary, multi-category, continuous, and longitudinal.
• Integrates multiple estimation approaches including parametric modeling, optimization, and machine learning.
• Provides tools for weight assessment and weighted regression with uncertainty quantification.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Noah Greifer

Collection Method

Software package for generating statistical weights.

Time Range

Temporal coverage is not specified.

Freshness

Update frequency or recency is unknown.

Geography

Spatial coverage is not specified.