R package for implementing counterfactual estimators in panel fixed-effect settings. It is suitable for panel/TSCS analysis with binary treatments under (hypothetically) baseline randomization. It allows a treatment to switch on and off and limited carryover effects. It supports linear factor models—hence, a generalization of gsynth—and the matrix completion method.
Repo: GitHub (1.0.0)
Examples: R code used in the tutorial can be downloaded from here.
Reference: Licheng Liu, Ye Wang, Yiqing Xu (2021). A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data. American Journal of Political Science, conditionally accepted.
You can install fect directly from CRAN by typing the following command in the R console:
install.packages('fect')You can install the development version of fect from GitHub by typing the following commands:
devtools::install_github('xuyiqing/fect')panelview for panel data visualization is also highly recommended:
devtools::install_github('xuyiqing/panelView')fect depends on the following packages, which will be installed automatically when fect is being installed. You can also install them manually.
## for processing C++ code
require(Rcpp)
## for plotting
require(ggplot2)
require(GGally)
require(grid)
require(gridExtra)
## for parallel computing
require(foreach)
require(future)
require(doParallel)
require(abind) - Mac users who have updated to MacOS BigSur or Monterey will likely encounter compilation problems. See here for a potential solution.
- Windows users please consider upgrading R to 4.0.0 or higher and installing the latest Rtools to avoid C++17 complier errors when installing fastplm.
- For Rcpp, RcppArmadillo and MacOS “-lgfortran” and “-lquadmath” error, click here for details.
- Installation failure related to OpenMP on MacOS, click here for a solution.
- To fix these issues, try installing gfortran from here.
Please report bugs to yiqingxu [at] stanford.edu with your sample code and data file. Much appreciated!