Provides a computationally efficient way of fitting
weighted linear fixed effects estimators for causal
inference with various weighting schemes. Weighted linear
fixed effects estimators can be used to estimate the
average treatment effects under different identification
strategies. This includes stratified randomized
experiments, matching and stratification for
observational studies, first differencing, and
difference-in-differences. The package implements methods
described in Imai and Kim (2017) "When should We Use
Linear Fixed Effects Regression Models for Causal
Inference with Longitudinal Data?", available at
|Author||In Song Kim [aut, cre], Kosuke Imai [aut], Erik Wang [aut]|
|Date of publication||2017-07-18 22:05:36 UTC|
|Maintainer||In Song Kim <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.