MatchIt - Nonparametric Preprocessing for Parametric Causal Inference

Selects matched samples of the original treated and control groups with similar covariate distributions -- can be used to match exactly on covariates, to match on propensity scores, or perform a variety of other matching procedures. The package also implements a series of recommendations offered in Ho, Imai, King, and Stuart (2007) <DOI:10.1093/pan/mpl013>. (The 'gurobi' package, which is not on CRAN, is optional and comes with an installation of the Gurobi Optimizer, available at <https://www.gurobi.com>.)

Last updated 8 days ago

14.73 score 210 stars 20 packages 2.5k scripts 18k downloads

cobalt - Covariate Balance Tables and Plots

Generate balance tables and plots for covariates of groups preprocessed through matching, weighting or subclassification, for example, using propensity scores. Includes integration with 'MatchIt', 'WeightIt', 'MatchThem', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', 'cem', 'sbw', and 'designmatch' for assessing balance on the output of their preprocessing functions. Users can also specify data for balance assessment not generated through the above packages. Also included are methods for assessing balance in clustered or multiply imputed data sets or data sets with multi-category, continuous, or longitudinal treatments.

Last updated 8 months ago

causal-inferencepropensity-scores

13.14 score 73 stars 8 packages 792 scripts 8.7k downloads

WeightIt - Weighting for Covariate Balance in Observational Studies

Generates balancing weights for causal effect estimation in observational studies with binary, multi-category, or continuous point or longitudinal treatments by easing and extending the functionality of several R packages and providing in-house estimation methods. Available methods include those that rely on parametric modeling, optimization, and machine learning. Also allows for assessment of weights and checking of covariate balance by interfacing directly with the 'cobalt' package. Methods for estimating weighted regression models that take into account uncertainty in the estimation of the weights via M-estimation or bootstrapping are available. See the vignette "Installing Supporting Packages" for instructions on how to install any package 'WeightIt' uses, including those that may not be on CRAN.

Last updated 9 days ago

causal-inferenceinverse-probability-weightsobservational-studypropensity-scores

11.36 score 102 stars 3 packages 444 scripts 5.5k downloads

clarify - Simulation-Based Inference for Regression Models

Performs simulation-based inference as an alternative to the delta method for obtaining valid confidence intervals and p-values for regression post-estimation quantities, such as average marginal effects and predictions at representative values. This framework for simulation-based inference is especially useful when the resulting quantity is not normally distributed and the delta method approximation fails. The methodology is described in King, Tomz, and Wittenberg (2000) <doi:10.2307/2669316>. 'clarify' is meant to replace some of the functionality of the archived package 'Zelig'; see the vignette "Translating Zelig to clarify" for replicating this functionality.

Last updated 6 months ago

5.95 score 22 stars 37 scripts 1.1k downloads

lmw - Linear Model Weights

Computes the implied weights of linear regression models for estimating average causal effects and provides diagnostics based on these weights. These diagnostics rely on the analyses in Chattopadhyay and Zubizarreta (2023) <doi:10.1093/biomet/asac058> where several regression estimators are represented as weighting estimators, in connection to inverse probability weighting. 'lmw' provides tools to diagnose representativeness, balance, extrapolation, and influence for these models, clarifying the target population of inference. Tools are also available to simplify estimating treatment effects for specific target populations of interest.

Last updated 10 months ago

4.47 score 11 stars 18 scripts 171 downloads

fwb - Fractional Weighted Bootstrap

An implementation of the fractional weighted bootstrap to be used as a drop-in for functions in the 'boot' package. The fractional weighted bootstrap (also known as the Bayesian bootstrap) involves drawing weights randomly that are applied to the data rather than resampling units from the data. See Xu et al. (2020) <doi:10.1080/00031305.2020.1731599> for details.

Last updated 4 months ago

4.10 score 6 stars 1 scripts 2.1k downloads

optweight - Targeted Stable Balancing Weights Using Optimization

Use optimization to estimate weights that balance covariates for binary, multinomial, and continuous treatments in the spirit of Zubizarreta (2015) <doi:10.1080/01621459.2015.1023805>. The degree of balance can be specified for each covariate. In addition, sampling weights can be estimated that allow a sample to generalize to a population specified with given target moments of covariates.

Last updated 2 years ago

causal-inferenceinverse-probability-weightsobservational-studyoptimizationpropensity-scores

3.78 score 8 stars 15 scripts 1.0k downloads