R/data_gen_p2.R
In JiayiJi/causal.multiple.treatments: Estimation of Causal Effects of Multiple Treatments with a Binary Outcome
Defines functions
data_gen_p2
data_gen_p2 = function(n = 11600, p =10, overlap = "weak", all_confounder = T ) {
#generate treatment label W
W = sample(c(1,2,3), size=n, replace=T, prob=c(.034,.517,.449))
#generate covariates distribution conditional on treatment
#==================================#
#===========weak overlap===========#
#==================================#
if (overlap == "weak") {
Xcon<-matrix(NA, nrow=n, ncol=5); Xcat<-matrix(NA, nrow=n, ncol=5);
for (i in 1:n){
Xcon[i,] = matrix(rnorm(5,0,1)*(W[i]==1) + rnorm(5, 1, 1)*(W[i]==2) + rnorm(5, 2, 1)*(W[i]==3),ncol=5);
Xcat[i,] = matrix(sample (c(1,2,3), size = 5,replace =T, prob = c(.3,.3,.4))*(W[i]==1)
+ sample (c(1,2,3), size = 5,replace =T, prob = c(.6,.2,.2))*(W[i]==2)
+ sample (c(1,2,3), size = 5,replace =T, prob = c(.8,.1,.1))*(W[i]==3),ncol=5)
}
x1 = Xcon[,1]; x2 = Xcon[,2]; x3 = Xcon[,3]; x4 = Xcon[,4]; x5 = Xcon[,5];
x6 = Xcat[,1]; x7 = Xcat[,2]; x8 = Xcat[,3]; x9 = Xcat[,4]; x10 = Xcat[,5]
trtdat<- data.frame(W,Xcon, Xcat)
#*************************#
#draw potential outcomes
tau1 = .1; tau2 = .6; tau3= 1.0
Yp1 = expit(tau1+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 + 0.2*x6 + 0.4*x7 - 0.6*x8 + 0.2*x9 +0.2*x10
-.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 + .4*x1*x7 - 0.5*x2*x8 + .2*x6*x9 + .27*x4^3 )
Yp2 = expit(tau2+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 + 0.2*x6 + 0.4*x7 - 0.6*x8 + 0.2*x9 + 0.2*x10
-.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 + .4*x1*x7 - 0.5*x2*x8 + .2*x6*x9 + .27*x4^3 )
Yp3 = expit(tau3+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 + 0.2*x6 + 0.4*x7 - 0.6*x8 + 0.2*x9 + 0.2*x10
-.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 + .4*x1*x7 - 0.5*x2*x8 + .2*x6*x9 + .27*x4^3 )
#potential outcomes
Y1 = Y2 = Y3= NULL
for (i in 1:n) {
Y1[i] = rbinom(1, 1, Yp1[i])
Y2[i] = rbinom(1, 1, Yp2[i])
Y3[i] = rbinom(1, 1, Yp3[i])}
# table(Y1)[2]/sum(table(Y1))
# table(Y2)[2]/sum(table(Y2))
# table(Y3)[2]/sum(table(Y3))
# observed outcomes
Y = cbind(Y1,Y2,Y3)
YW = cbind(Y,W)
Yobs = apply(YW, 1, function(x) x[1:3][x[4]])
}
#==================================#
#=========strong overlap===========#
#==================================#
if (overlap == "strong") {
Xcon<-matrix(NA, nrow=n, ncol=5); Xcat<-matrix(NA, nrow=n, ncol=5);
for (i in 1:n) {
Xcon[i,] = matrix(rnorm(5, mean = 0.05*W[i], sd = 1- 0.05*W[i]),ncol=5);
Xcat[i,] = matrix(sample (c(1,2,3), size = 5,replace =T, prob = c(.3-0.001*W[i],.3+.001*W[i],.4)),ncol=5)
}
x1 = Xcon[,1]; x2 = Xcon[,2]; x3 = Xcon[,3]; x4 = Xcon[,4]; x5 = Xcon[,5];
x6 = Xcat[,1]; x7 = Xcat[,2]; x8 = Xcat[,3]; x9 = Xcat[,4]; x10 = Xcat[,5]
trtdat<- data.frame(W, Xcon, Xcat)
#********************************#
#draw potential outcomes
tau1 = .1; tau2 = .6; tau3= 1.0
Yp1 = expit(tau1+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 + 0.2*x6 + 0.4*x7 - 0.6*x8 + 0.2*x9 +0.2*x10
-.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 + .4*x1*x7 - 0.5*x2*x8 + .2*x6*x9 + .27*x4^3 )
Yp2 = expit(tau2+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 + 0.2*x6 + 0.4*x7 - 0.6*x8 + 0.2*x9 + 0.2*x10
-.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 + .4*x1*x7 - 0.5*x2*x8 + .2*x6*x9 + .27*x4^3 )
Yp3 = expit(tau3+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 + 0.2*x6 + 0.4*x7 - 0.6*x8 + 0.2*x9 + 0.2*x10
-.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 + .4*x1*x7 - 0.5*x2*x8 + .2*x6*x9 + .27*x4^3 )
#potential outcomes
Y1 = Y2 = Y3= NULL
for (i in 1:n) {
Y1[i] = rbinom(1, 1, Yp1[i])
Y2[i] = rbinom(1, 1, Yp2[i])
Y3[i] = rbinom(1, 1, Yp3[i])}
# observed outcomes
Y = cbind(Y1,Y2,Y3)
YW = cbind(Y,W)
Yobs = apply(YW, 1, function(x) x[1:3][x[4]])
}
#==================================#
#========moderate overlap==========#
#==================================#
if (overlap == "moderate"){
Xcon<-matrix(NA, nrow=n, ncol=5); Xcat<-matrix(NA, nrow=n, ncol=5)
for (i in 1:n) {
Xcon[i,] = matrix(rnorm(5, mean = 0.05*W[i], sd = 1- 0.05*W[i]),ncol=5);
Xcat[i,] = matrix(sample (c(1,2,3), size = 5,replace =T, prob = c(.3,.3,.4))*(W[i]==1)
+ sample (c(1,2,3), size = 5,replace =T, prob = c(.6,.2,.2))*(W[i]==2)
+ sample (c(1,2,3), size = 5,replace =T, prob = c(.8,.1,.1))*(W[i]==3),ncol=5)
}
x1 = Xcon[,1]; x2 = Xcon[,2]; x3 = Xcon[,3]; x4 = Xcon[,4]; x5 = Xcon[,5];
x6 = Xcat[,1]; x7 = Xcat[,2]; x8 = Xcat[,3]; x9 = Xcat[,4]; x10 = Xcat[,5]
trtdat<- data.frame(W, Xcon, Xcat)
if (all_confounder == T) {
tau1 = .1; tau2 = .3; tau3= 0.5
Yp1 = expit(tau1+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 + 0.2*x6 + 0.4*x7 - 0.6*x8 + 0.2*x9 -0.3*x10
-.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 + .4*x1*x7 - 0.5*x2*x8 + .2*x6*x9 + .1*x4^3 -.2*x10^2)
Yp2 = expit(tau2+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 + 0.2*x6 + 0.4*x7 - 0.6*x8 + 0.2*x9 - 0.3*x10
-.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 + .4*x1*x7 - 0.5*x2*x8 + .2*x6*x9 + .1*x4^3 -.2*x10^2)
Yp3 = expit(tau3+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 + 0.2*x6 + 0.4*x7 - 0.6*x8 + 0.2*x9- 0.3*x10
-.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 + .4*x1*x7 - 0.5*x2*x8 + .2*x6*x9 + .1*x4^3 -.2*x10^2)
#potential outcomes
Y1 = Y2 = Y3= NULL
for (i in 1:n) {
Y1[i] = rbinom(1, 1, Yp1[i])
Y2[i] = rbinom(1, 1, Yp2[i])
Y3[i] = rbinom(1, 1, Yp3[i])}
# observed outcomes
Y = cbind(Y1,Y2,Y3)
YW = cbind(Y,W)
Yobs = apply(YW, 1, function(x) x[1:3][x[4]])
}
if (all_confounder == F) {
tau1 = .2; tau2 = 0.4; tau3= 0.5
Yp1 = expit(tau1+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 -.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 - 0.5*x2*x5 + .23*x4^3 )
Yp2 = expit(tau2+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 -.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 - 0.5*x2*x5 + .23*x4^3 )
Yp3 = expit(tau3+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 -.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 - 0.5*x2*x5 + .23*x4^3 )
#potential outcomes
Y1 = Y2 = Y3= NULL
for (i in 1:n) {
Y1[i] = rbinom(1, 1, Yp1[i])
Y2[i] = rbinom(1, 1, Yp2[i])
Y3[i] = rbinom(1, 1, Yp3[i])}
# observed outcomes
Y = cbind(Y1,Y2,Y3)
YW = cbind(Y,W)
Yobs = apply(YW, 1, function(x) x[1:3][x[4]])
}
}
#need the following two lines if run GBM; ignore if run BART
colnames(trtdat)<- c("trt_ind", "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10")
trtdat$trt_ind<- as.factor(trtdat$trt_ind)
###########################################################
# true treatment effects
# ATE(1,2), ATE(1,3), ATE(2,3)
ATE12_RD = mean(Y[,1]) - mean(Y[,2])
ATE13_RD = mean(Y[,1]) - mean(Y[,3])
ATE23_RD = mean(Y[,2]) - mean(Y[,3])
ATE12_RR = mean(Y[,1]) / mean(Y[,2])
ATE13_RR = mean(Y[,1]) / mean(Y[,3])
ATE23_RR = mean(Y[,2]) / mean(Y[,3])
ATE12_OR = mean(Y[,1])/(1-mean(Y[,1])) / (mean(Y[,2])/(1-mean(Y[,2])))
ATE13_OR = mean(Y[,1])/(1-mean(Y[,1])) / (mean(Y[,3])/(1-mean(Y[,3])))
ATE23_OR = mean(Y[,2])/(1-mean(Y[,2])) / (mean(Y[,3])/(1-mean(Y[,3])))
# ATT(1,2), ATT(1,3)
ATT12_RD = mean(Y[W==1,1]) - mean(Y[W==1,2])
ATT13_RD = mean(Y[W==1,1]) - mean(Y[W==1,3])
ATT12_RR = mean(Y[W==1,1]) / mean(Y[W==1,2])
ATT13_RR = mean(Y[W==1,1]) / mean(Y[W==1,3])
ATT12_OR = mean(Y[W==1,1])/(1-mean(Y[W==1,1])) / (mean(Y[W==1,2])/(1-mean(Y[W==1,2])))
ATT13_OR = mean(Y[W==1,1])/(1-mean(Y[W==1,1])) / (mean(Y[W==1,3])/(1-mean(Y[W==1,3])))
Ests = list(ATE12_RD=ATE12_RD, ATE13_RD=ATE13_RD, ATE23_RD=ATE23_RD,ATE12_RR=ATE12_RR,ATE13_RR=ATE13_RR, ATE23_RR=ATE23_RR, ATE12_OR=ATE12_OR, ATE13_OR=ATE13_OR, ATE23_OR=ATE23_OR,
ATT12_RD=ATT12_RD, ATT13_RD=ATT13_RD, ATT12_RR=ATT12_RR, ATT13_RR=ATT13_RR, ATT12_OR=ATT12_OR, ATT13_OR=ATT13_OR)
return(list(trtdat = trtdat, Y=Y, Yobs=Yobs, n=n, Ests=Ests))
}
JiayiJi/causal.multiple.treatments documentation built on Nov. 14, 2019, 7:46 p.m.
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Defines functions data_gen_p2
data_gen_p2 = function(n = 11600, p =10, overlap = "weak", all_confounder = T ) {
#generate treatment label W
W = sample(c(1,2,3), size=n, replace=T, prob=c(.034,.517,.449))
#generate covariates distribution conditional on treatment
#==================================#
#===========weak overlap===========#
#==================================#
if (overlap == "weak") {
Xcon<-matrix(NA, nrow=n, ncol=5); Xcat<-matrix(NA, nrow=n, ncol=5);
for (i in 1:n){
Xcon[i,] = matrix(rnorm(5,0,1)*(W[i]==1) + rnorm(5, 1, 1)*(W[i]==2) + rnorm(5, 2, 1)*(W[i]==3),ncol=5);
Xcat[i,] = matrix(sample (c(1,2,3), size = 5,replace =T, prob = c(.3,.3,.4))*(W[i]==1)
+ sample (c(1,2,3), size = 5,replace =T, prob = c(.6,.2,.2))*(W[i]==2)
+ sample (c(1,2,3), size = 5,replace =T, prob = c(.8,.1,.1))*(W[i]==3),ncol=5)
}
x1 = Xcon[,1]; x2 = Xcon[,2]; x3 = Xcon[,3]; x4 = Xcon[,4]; x5 = Xcon[,5];
x6 = Xcat[,1]; x7 = Xcat[,2]; x8 = Xcat[,3]; x9 = Xcat[,4]; x10 = Xcat[,5]
trtdat<- data.frame(W,Xcon, Xcat)
#*************************#
#draw potential outcomes
tau1 = .1; tau2 = .6; tau3= 1.0
Yp1 = expit(tau1+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 + 0.2*x6 + 0.4*x7 - 0.6*x8 + 0.2*x9 +0.2*x10
-.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 + .4*x1*x7 - 0.5*x2*x8 + .2*x6*x9 + .27*x4^3 )
Yp2 = expit(tau2+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 + 0.2*x6 + 0.4*x7 - 0.6*x8 + 0.2*x9 + 0.2*x10
-.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 + .4*x1*x7 - 0.5*x2*x8 + .2*x6*x9 + .27*x4^3 )
Yp3 = expit(tau3+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 + 0.2*x6 + 0.4*x7 - 0.6*x8 + 0.2*x9 + 0.2*x10
-.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 + .4*x1*x7 - 0.5*x2*x8 + .2*x6*x9 + .27*x4^3 )
#potential outcomes
Y1 = Y2 = Y3= NULL
for (i in 1:n) {
Y1[i] = rbinom(1, 1, Yp1[i])
Y2[i] = rbinom(1, 1, Yp2[i])
Y3[i] = rbinom(1, 1, Yp3[i])}
# table(Y1)[2]/sum(table(Y1))
# table(Y2)[2]/sum(table(Y2))
# table(Y3)[2]/sum(table(Y3))
# observed outcomes
Y = cbind(Y1,Y2,Y3)
YW = cbind(Y,W)
Yobs = apply(YW, 1, function(x) x[1:3][x[4]])
}
#==================================#
#=========strong overlap===========#
#==================================#
if (overlap == "strong") {
Xcon<-matrix(NA, nrow=n, ncol=5); Xcat<-matrix(NA, nrow=n, ncol=5);
for (i in 1:n) {
Xcon[i,] = matrix(rnorm(5, mean = 0.05*W[i], sd = 1- 0.05*W[i]),ncol=5);
Xcat[i,] = matrix(sample (c(1,2,3), size = 5,replace =T, prob = c(.3-0.001*W[i],.3+.001*W[i],.4)),ncol=5)
}
x1 = Xcon[,1]; x2 = Xcon[,2]; x3 = Xcon[,3]; x4 = Xcon[,4]; x5 = Xcon[,5];
x6 = Xcat[,1]; x7 = Xcat[,2]; x8 = Xcat[,3]; x9 = Xcat[,4]; x10 = Xcat[,5]
trtdat<- data.frame(W, Xcon, Xcat)
#********************************#
#draw potential outcomes
tau1 = .1; tau2 = .6; tau3= 1.0
Yp1 = expit(tau1+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 + 0.2*x6 + 0.4*x7 - 0.6*x8 + 0.2*x9 +0.2*x10
-.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 + .4*x1*x7 - 0.5*x2*x8 + .2*x6*x9 + .27*x4^3 )
Yp2 = expit(tau2+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 + 0.2*x6 + 0.4*x7 - 0.6*x8 + 0.2*x9 + 0.2*x10
-.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 + .4*x1*x7 - 0.5*x2*x8 + .2*x6*x9 + .27*x4^3 )
Yp3 = expit(tau3+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 + 0.2*x6 + 0.4*x7 - 0.6*x8 + 0.2*x9 + 0.2*x10
-.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 + .4*x1*x7 - 0.5*x2*x8 + .2*x6*x9 + .27*x4^3 )
#potential outcomes
Y1 = Y2 = Y3= NULL
for (i in 1:n) {
Y1[i] = rbinom(1, 1, Yp1[i])
Y2[i] = rbinom(1, 1, Yp2[i])
Y3[i] = rbinom(1, 1, Yp3[i])}
# observed outcomes
Y = cbind(Y1,Y2,Y3)
YW = cbind(Y,W)
Yobs = apply(YW, 1, function(x) x[1:3][x[4]])
}
#==================================#
#========moderate overlap==========#
#==================================#
if (overlap == "moderate"){
Xcon<-matrix(NA, nrow=n, ncol=5); Xcat<-matrix(NA, nrow=n, ncol=5)
for (i in 1:n) {
Xcon[i,] = matrix(rnorm(5, mean = 0.05*W[i], sd = 1- 0.05*W[i]),ncol=5);
Xcat[i,] = matrix(sample (c(1,2,3), size = 5,replace =T, prob = c(.3,.3,.4))*(W[i]==1)
+ sample (c(1,2,3), size = 5,replace =T, prob = c(.6,.2,.2))*(W[i]==2)
+ sample (c(1,2,3), size = 5,replace =T, prob = c(.8,.1,.1))*(W[i]==3),ncol=5)
}
x1 = Xcon[,1]; x2 = Xcon[,2]; x3 = Xcon[,3]; x4 = Xcon[,4]; x5 = Xcon[,5];
x6 = Xcat[,1]; x7 = Xcat[,2]; x8 = Xcat[,3]; x9 = Xcat[,4]; x10 = Xcat[,5]
trtdat<- data.frame(W, Xcon, Xcat)
if (all_confounder == T) {
tau1 = .1; tau2 = .3; tau3= 0.5
Yp1 = expit(tau1+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 + 0.2*x6 + 0.4*x7 - 0.6*x8 + 0.2*x9 -0.3*x10
-.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 + .4*x1*x7 - 0.5*x2*x8 + .2*x6*x9 + .1*x4^3 -.2*x10^2)
Yp2 = expit(tau2+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 + 0.2*x6 + 0.4*x7 - 0.6*x8 + 0.2*x9 - 0.3*x10
-.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 + .4*x1*x7 - 0.5*x2*x8 + .2*x6*x9 + .1*x4^3 -.2*x10^2)
Yp3 = expit(tau3+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 + 0.2*x6 + 0.4*x7 - 0.6*x8 + 0.2*x9- 0.3*x10
-.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 + .4*x1*x7 - 0.5*x2*x8 + .2*x6*x9 + .1*x4^3 -.2*x10^2)
#potential outcomes
Y1 = Y2 = Y3= NULL
for (i in 1:n) {
Y1[i] = rbinom(1, 1, Yp1[i])
Y2[i] = rbinom(1, 1, Yp2[i])
Y3[i] = rbinom(1, 1, Yp3[i])}
# observed outcomes
Y = cbind(Y1,Y2,Y3)
YW = cbind(Y,W)
Yobs = apply(YW, 1, function(x) x[1:3][x[4]])
}
if (all_confounder == F) {
tau1 = .2; tau2 = 0.4; tau3= 0.5
Yp1 = expit(tau1+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 -.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 - 0.5*x2*x5 + .23*x4^3 )
Yp2 = expit(tau2+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 -.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 - 0.5*x2*x5 + .23*x4^3 )
Yp3 = expit(tau3+ .2*x1 + .3*x2 - .4*x3 + .2*x4 - .2*x5 -.6*x1^2 - 0.7*x2^2 - 0.6*x3^2 - 0.5*x2*x5 + .23*x4^3 )
#potential outcomes
Y1 = Y2 = Y3= NULL
for (i in 1:n) {
Y1[i] = rbinom(1, 1, Yp1[i])
Y2[i] = rbinom(1, 1, Yp2[i])
Y3[i] = rbinom(1, 1, Yp3[i])}
# observed outcomes
Y = cbind(Y1,Y2,Y3)
YW = cbind(Y,W)
Yobs = apply(YW, 1, function(x) x[1:3][x[4]])
}
}
#need the following two lines if run GBM; ignore if run BART
colnames(trtdat)<- c("trt_ind", "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10")
trtdat$trt_ind<- as.factor(trtdat$trt_ind)
###########################################################
# true treatment effects
# ATE(1,2), ATE(1,3), ATE(2,3)
ATE12_RD = mean(Y[,1]) - mean(Y[,2])
ATE13_RD = mean(Y[,1]) - mean(Y[,3])
ATE23_RD = mean(Y[,2]) - mean(Y[,3])
ATE12_RR = mean(Y[,1]) / mean(Y[,2])
ATE13_RR = mean(Y[,1]) / mean(Y[,3])
ATE23_RR = mean(Y[,2]) / mean(Y[,3])
ATE12_OR = mean(Y[,1])/(1-mean(Y[,1])) / (mean(Y[,2])/(1-mean(Y[,2])))
ATE13_OR = mean(Y[,1])/(1-mean(Y[,1])) / (mean(Y[,3])/(1-mean(Y[,3])))
ATE23_OR = mean(Y[,2])/(1-mean(Y[,2])) / (mean(Y[,3])/(1-mean(Y[,3])))
# ATT(1,2), ATT(1,3)
ATT12_RD = mean(Y[W==1,1]) - mean(Y[W==1,2])
ATT13_RD = mean(Y[W==1,1]) - mean(Y[W==1,3])
ATT12_RR = mean(Y[W==1,1]) / mean(Y[W==1,2])
ATT13_RR = mean(Y[W==1,1]) / mean(Y[W==1,3])
ATT12_OR = mean(Y[W==1,1])/(1-mean(Y[W==1,1])) / (mean(Y[W==1,2])/(1-mean(Y[W==1,2])))
ATT13_OR = mean(Y[W==1,1])/(1-mean(Y[W==1,1])) / (mean(Y[W==1,3])/(1-mean(Y[W==1,3])))
Ests = list(ATE12_RD=ATE12_RD, ATE13_RD=ATE13_RD, ATE23_RD=ATE23_RD,ATE12_RR=ATE12_RR,ATE13_RR=ATE13_RR, ATE23_RR=ATE23_RR, ATE12_OR=ATE12_OR, ATE13_OR=ATE13_OR, ATE23_OR=ATE23_OR,
ATT12_RD=ATT12_RD, ATT13_RD=ATT13_RD, ATT12_RR=ATT12_RR, ATT13_RR=ATT13_RR, ATT12_OR=ATT12_OR, ATT13_OR=ATT13_OR)
return(list(trtdat = trtdat, Y=Y, Yobs=Yobs, n=n, Ests=Ests))
}
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