quantile.R

library(Matching)

data(lalonde)

#### EASY QUANTILE EFFECTS FOR RCTs...
## MEDIAN EFFECT
quantile(lalonde$re78[lalonde$treat == 1], probs = 0.5) -
quantile(lalonde$re78[lalonde$treat == 0], probs = 0.5)

#### 0.9 QUANTILE EFFECT
quantile(lalonde$re78[lalonde$treat == 1], probs = 0.9) -
  quantile(lalonde$re78[lalonde$treat == 0], probs = 0.9)

    
#### 0.1 QUANTILE EFFECT    
quantile(lalonde$re78[lalonde$treat == 1], probs = 0.1) -
  quantile(lalonde$re78[lalonde$treat == 0], probs = 0.1)

##############
##############
## IF YOU WANT TO GET QUANTILE EFFECTS FOR MATCHED DATA
########################################################

X = cbind(lalonde$age, lalonde$educ, lalonde$black,
          lalonde$hisp, 
          lalonde$married, lalonde$nodegr, lalonde$u74,
          lalonde$u75, 
          lalonde$re75, lalonde$re74)

#The covariates we want to obtain balance on
BalanceMat <- X

genout <- GenMatch(Tr=lalonde$treat, X=X,  
                   pop.size=50, max.generations=10,
                   wait.generations=10)

mout <- Match(Tr = lalonde$treat, X = X, Weight.matrix = genout)


############
############

### CHECK BALANCE

MatchBalance(lalonde$treat ~ lalonde$age + lalonde$educ + 
             lalonde$black + 
             lalonde$hisp + lalonde$married +
             lalonde$nodegr + 
             lalonde$u74 + lalonde$u75 + lalonde$re74 + 
             lalonde$re75, 
             match.out = mout)
                     
MatchBalance(treat ~ age + educ + black + 
               hisp + married + nodegr + 
               u74 + u75 + re74 + re75, data = lalonde, 
               match.out = mout)

###########
###########

# MATCHED DATA

matched_data <- lalonde[c(mout$index.treated, mout$index.control),]

# the weights below only relate to matching ties (otherwise all weights are 1s)
matched_weights <- c(mout$weights, mout$weights) # weights that reflect matching ties

## Quantile effects for matched data that don't consider the weights (imprecise effects)
## Specifically, in the example below, the MEDIAN treatment effect for matched data
median(matched_data$re78[matched_data$treated == 1]) - median(matched_data$re78[matched_data$treated == 0])


############
############
# if you want to run a quantile regression... then you can use the weights and obtain precise effects
################
install.packages("quantreg")
library(quantreg)

# median treatment effect (tau = 0.5)
quant_reg1 <- rq(re78 ~ treat, data = matched_data, weights = matched_weights, tau = 0.5)
summary(quant_reg1)