dbw - Doubly Robust Distribution Balancing Weighting Estimation
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.
