Improved methods based on inverse probability weighting and outcome regression for causal inference and missing data problems.
The R package iWeigReg -- version 1.0 can be used for two main tasks:
to estimate the mean of an outcome in the presence of missing data,
to estimate the average treatment effect in causal inference.
There are 4 functions provided for the first task:
mn.lik: the non-calibrated (or non-doubly robust) likelihood estimator in Tan (2006),
mn.clik: the calibrated (or doubly robust) likelihood estimator in Tan (2010),
mn.reg: the non-calibrated (or non-doubly robust) regression estimator,
mn.creg: the calibrated (or doubly robust) regression estimator in Tan (2006).
In parallel, there are also 4 functions for the second task, ate.lik, ate.clik, ate.reg, and ate.creg. Currently, the treatment is assumed to be binary (i.e., untreated or treated). Extensions to multi-valued treatments will be incorporated in later versions.
In general, the function recommended to use is the calibrated (or doubly robust) likelihood estimator, mn.clik or ate.clik, which is a two-step procedure with the first step corresponding to the non-calibrated (or non-doubly robust) likelihood estimator. The calibrated (or doubly robust) regression estimator, mn.creg or ate.creg, is a close relative to the calibrated likelihood estimator, but may sometimes yield an estimate lying outside the sample range, for example, outside the unit interval (0,1) for estimating the mean of a binary outcome.
The package also provides two functions, mn.HT and ate.HT, for the Horvitz-Thompson estimator, i.e., the unaugmented inverse probability weighted estimator. These functions can be used for balance checking.
See the vignette for more details.