library(randomForest)
library(grf)
library(sufrep)
# install R packages if needed
# install.packages("randomForest")
# install.packages("grf")
install.packages("sufrep")
devtools::install_github("grf-labs/sufrep")
library(sufrep)
path <- "C:/Users/wb614536/OneDrive - WBG/Documents/Reproducibility/20231030-climate-immobility/files submitted/Reproducibility package/"
# load the dataset
dataset <- read.csv(paste(path,"Datasets/dataset.csv", sep = "")) # please change working directory to load this file
names(dataset)
# Causal forest analysis (Wager & Athey, 2018; Athey et al., 2019)
set.seed(30102023) # set seed for replication; seed is today's date
# 1) Orthogonalization
dataset$hhid <- as.factor(dataset$hhid)
dataset$spei_gs <- -(dataset$spei_gs) # invert sign of the SPEI variable to improve interpretability
dataset$l1spei_gs <- -(dataset$l1spei_gs) # invert sign of the lagged SPEI variable to improve interpretability
encoder <- make_encoder(dataset[, c(8, 9, 21, 23, 25, 27, 29, 30, 31)], dataset$hhid, method="means") # to incorporate household fixed effects, we use the sufrep package implementing the method developed in Johannemann et al. (2019)
