wfe

Weighted Linear Fixed Effects Regression Models for Causal Inference

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 <https://imai.fas.harvard.edu/research/FEmatch.html>.

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Description file content

Package
wfe
Type
Package
Title
Weighted Linear Fixed Effects Regression Models for Causal Inference
Version
1.8
Date
2019-01-09
Description
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 .
License
GPL (>= 2)
Imports
utils, arm, Matrix, MASS, methods
Depends
R (>= 3.2.0)
Encoding
UTF-8
LazyData
true
BugReports
https://github.com/insongkim/wfe/issues
NeedsCompilation
yes
Packaged
2019-01-09 11:43:18 UTC; insong
Author
In Song Kim [aut, cre], Kosuke Imai [aut]
Maintainer
In Song Kim
Repository
CRAN
Date/Publication
2019-01-09 12:00:03 UTC

install.packages('wfe')

1.8

12 days ago

In Song Kim

GPL (>= 2)

Depends on

R (>= 3.2.0)

Imports

utils, arm, Matrix, MASS, methods

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