| iWeigReg-package {iWeigReg} | R Documentation |
A R package for improved methods for causal inference and missing data problems
Description
Improved methods based on inverse probability weighting and outcome regression for causal inference and missing data problems.
Details
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.