psace: Multiple robust estimators of average causal effects within...

In shuyang1987/psace: Multiply Robust Estimators of Average Causal Effects within principal strata (PSACE)

Description

Implements various estimators of PS-ACE from randomized experiments and observational studies

Usage

psace(X, Z, S, Y, family.Y, nboot = 0)

Arguments

X	is a matrix of pre-treatment covariates without intercept (n x p).
Z	is a vector of treatment (n x 1).
S	is a vector of binary intermediate outcome (n x 1).
Y	is a vector of outcome ( n x 1).
family.Y	specifies the family for the outcome model. "gaussian": a linear regression model for the continuous outcome. "binomial": a logistic regression model for the binary outcome.
nboot	is the number of bootstrap samples.

Details

Details will be provided in the reference paper.

Value

• tau10w: a principal score weighting estimator of tau10

• tau10sw: a stabilized weighting estimator of tau10

• tau10reg: a regression estimator of tau10 using outcome mean regression and inverse probability of treatment weighting

• tau10reg2: a regression estimator of tau10 using outcome mean and principal score regression

• tau10aw: a triply robust estimator of tau10

• bootstrap variance estimator

  • ve.tau10w

  • ve.tau10sw

  • ve.tau10reg

  • ve.tau10reg2

  • ve.tau10aw

• tau00w: a principal score weighting estimator of tau00

• tau00sw: a stabilized weighting estimator of tau00

• tau00reg: a regression estimator of tau00 using outcome mean regression and inverse probability of treatment weighting

• tau00reg2: a regression estimator of tau00 using outcome mean and principal score regression

• tau00aw: a triply robust estimator of tau00

• bootstrap variance estimator

  • ve.tau00w

  • ve.tau00sw

  • ve.tau00reg

  • ve.tau00reg2

  • ve.tau00aw

• tau11w: a principal score weighting estimator of tau11

• tau11sw: a stabilized weighting estimator of tau11

• tau11reg: a regression estimator of tau11 using outcome mean regression and inverse probability of treatment weighting

• tau11reg2: a regression estimator of tau11 using outcome mean and principal score regression

• tau11aw: a triply robust estimator of tau11

• bootstrap variance estimator

  • ve.tau11w

  • ve.tau11sw

  • ve.tau11reg

  • ve.tau11reg2

  • ve.tau11aw

References

A reference paper will come up soon.

Examples

library(stats)

set.seed(1)
#X consists of 5 covariates
n <- 500
X <- rnorm(n,0.25,1)
for(k in 2:4){
 X <- cbind(X,rnorm(n,0.25,1))
}
X <- cbind(X,rbinom(n,1,0.5))

# treatment assignment
Xtilde1 <- (X-0.25)/1
theta <- 0
eta <- c(0,1,1,1,1,theta)/2.5
px0 <- exp(cbind(1,Xtilde1)%*%eta)
pix  <- px0/(1+px0)
Z   <- rbinom(n,1,pix)

# S-model
eta1 <- c( 2,-1,1,-1,1,theta)/2.5
eta0 <- c(-2,1,-1,1,-1,theta)/2.5
p1xtemp <- exp(cbind(1,Xtilde1)%*%eta1)
p0xtemp <- exp(cbind(1,Xtilde1)%*%eta0)
p1x <- p1xtemp/(1+p1xtemp)
p0x <- p0xtemp/(1+p0xtemp)
s1<-rbinom(n,1,p1x)
s0<-rbinom(n,1,p0x)
S<-s1*Z+s0*(1-Z)

# Y-model (continuous outcome)
Y1<- Xtilde1%*%rep(1,5)*(1+S+Z) +rnorm(n)
# Y-model (binary outcome)
leYtemp <- Xtilde1%*%rep(1,5)*(1+S+Z)/4
eY<-exp(leYtemp)/(1+exp(leYtemp) )
Y2<-rbinom(n,1,eY)

out1<-psace(X,Z,S,Y1,family.Y="gaussian",nboot=50)
out1$tau10aw
out1$ve.tau10aw

out2<-psace(X,Z,S,Y2,family.Y="binomial",nboot=50)
out2$tau10aw
out2$ve.tau10aw

shuyang1987/psace documentation built on May 6, 2020, 12:37 a.m.