TSHT {RobustIV} | R Documentation |
Perform Two-Stage Hard Thresholding method, which provides the robust inference of the treatment effect in the presence of invalid instrumental variables.
TSHT(
Y,
D,
Z,
X,
intercept = TRUE,
method = c("OLS", "DeLasso", "Fast.DeLasso"),
voting = c("MaxClique", "MP", "Conservative"),
robust = TRUE,
alpha = 0.05,
tuning.1st = NULL,
tuning.2nd = NULL
)
Y |
The outcome observation, a vector of length |
D |
The treatment observation, a vector of length |
Z |
The instrument observation of dimension |
X |
The covariates observation of dimension |
intercept |
Whether the intercept is included. (default = |
method |
The method used to estimate the reduced form parameters. |
voting |
The voting option used to estimate valid IVs. |
robust |
If |
alpha |
The significance level for the confidence interval. (default = |
tuning.1st |
The tuning parameter used in the 1st stage to select relevant instruments. If |
tuning.2nd |
The tuning parameter used in the 2nd stage to select valid instruments. If |
When robust = TRUE
, the method
will be input as ’OLS’
.
When voting = MaxClique
and there are multiple maximum cliques, betaHat
,beta.sdHat
,ci
, and VHat
will be list objects
where each element of list corresponds to each maximum clique.
As for tuning parameter in the 1st stage and 2nd stage, if do not specify, for method "OLS" we adopt \sqrt{\log n}
for both tuning parameters, and for other methods
we adopt \max{(\sqrt{2.01 \log p_z}, \sqrt{\log n})}
for both tuning parameters.
TSHT
returns an object of class "TSHT", which is a list containing the following components:
betaHat |
The estimate of treatment effect. |
beta.sdHat |
The estimated standard error of |
ci |
The 1-alpha confidence interval for |
SHat |
The set of selected relevant IVs. |
VHat |
The set of selected relevant and valid IVs. |
voting.mat |
The voting matrix. |
check |
The indicator that the majority rule is satisfied. |
Guo, Z., Kang, H., Tony Cai, T. and Small, D.S. (2018), Confidence intervals for causal effects with invalid instruments by using two-stage hard thresholding with voting, J. R. Stat. Soc. B, 80: 793-815.
data("lineardata")
Y <- lineardata[,"Y"]
D <- lineardata[,"D"]
Z <- as.matrix(lineardata[,c("Z.1","Z.2","Z.3","Z.4","Z.5","Z.6","Z.7","Z.8")])
X <- as.matrix(lineardata[,c("age","sex")])
TSHT.model <- TSHT(Y=Y,D=D,Z=Z,X=X)
summary(TSHT.model)