dbw

Implements the doubly robust distribution balancing weighting proposed by Katsumata (2024) doi:10.1017/psrm.2024.23, 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) doi:10.1111/rssb.12027 and the entropy balancing weighting proposed by Hainmueller (2012) doi:10.1093/pan/mpr025, 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.

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