dbw

dbw is an R package for the doubly robust distribution balancing weighting (DBW) proposed by Katsumata (2025), which improves the augmented inverse probability weighting (AIPW) by estimating propensity scores with estimating equations suitable for the pre-specified parameter of interest (e.g., the average treatment effects or the average treatment effects on the treated) and estimating outcome models with the estimated inverse probability weights. 

 It also implements the covariate balancing propensity score proposed by Imai and Ratkovic (2014) and the entropy balancing weighting proposed by Hainmueller (2012), both of which use covariate balancing conditions in propensity score estimation. The point estimate of the parameter of interest and its uncertainty as well as coefficients for propensity score estimation and outcome regression are produced using the M-estimation. The same functions can be used to estimate average outcomes in missing outcome cases. It also provides several tools for summarizing and checking the estimation results, including covariate balance checks.

nawtilus

This package is replaced by dbw package above.

nawtilus is an R package for the navigated weighting (NAWT) proposed by Katsumata (2020), which estimates a pre-specified parameter of interest (e.g., the average treatment effects or the average treatment effects on the treated) with the inverse probability weighting where propensity scores are estimated using estimating equations suitable for the parameter of interest. It also provides several tools for summarizing and checking the estimation results, including covariate balance check and an inverse probability weights plot.

conjointIRT

under development