Description Usage Format Value References Examples
The function provides the estimation of ATE given the outcome Y, binary treatment T, and covariates X.
1 | ATE.DCB(Y,T,X)
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The function has the following parameters
Y
Outcome
T
Binary treatment status indicator: 1=treatment; 0=control.
X
Covariate matrix
ATT
Whether estimating ATT. Default is FALSE. If ATT=TRUE, it is equivalent to function “ATT.DCB”
aug
Whether augmentation. Default is FALSE. If aug=TRUE, it may include second-order moments.
lambda
Penalty on the loss of outcome regression on covariates in the control group. Default is 10.
delta
Penalty on the L2-norm of weight for each unit. Default is 0.001
mu
Penalty on the L2-norm of coefficient for each covariate. Default is 0.001
upsilon
Penalty on the L1-norm of coefficient for each covariate. Default is 0.001
thold
Stop criteria of the update process. Default is 1e-4.
max_iter
Maximum number of iterations. Default is 100000.
beta |
Covariate coefficient while estimating ATT weights |
beta1 |
Covariate coefficient while estimating ATC weights |
wt |
Estimated weight for each unit |
E[Y(1)] |
Expectation of outcome if treated |
E[Y(0)] |
Expectation of outcome if untreated |
ATE |
Estimation of ATE |
Kuang, K., Cui, P., Li, B., Jiang, M., & Yang, S. (2017). Estimating treatment effect in the wild via differentiated confounder balancing. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 265-274).
1 2 3 4 5 6 7 8 9 10 11 12 | set.seed(25)
n <- 200
Z <- matrix(rnorm(4*n),ncol=4,nrow=n)
prop <- 1 / (1 + exp(Z[,1] - 0.5 * Z[,2] + 0.25*Z[,3] + 0.1 * Z[,4]))
treat <- rbinom(n, 1, prop)
Y <- 200 + 10*treat+ (1.5*treat-0.5)*(27.4*Z[,1] + 13.7*Z[,2] + 13.7*Z[,3] + 13.7*Z[,4]) + rnorm(n)
X <- cbind(exp(Z[,1])/2,Z[,2]/(1+exp(Z[,1])), (Z[,1]*Z[,3]/25+0.6)^3,(Z[,2]+Z[,4]+20)^2)
fit1 <- ATE.DCB(Y,T,X)
fit1$ATE
fit2 <- ATE.DCB(Y,T,X,ATT=TRUE)
fit2$ATT
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