SpotIV: SpotIV method for causal inference in semi-parametric outcome...

In controlfunctionIV: Control Function Methods with Possibly Invalid Instrumental Variables

SpotIV method for causal inference in semi-parametric outcome model

Description

Perform causal inference in the semi-parametric outcome model with possibly invalid IVs.

Usage

SpotIV(
  Y,
  D,
  Z,
  X = NULL,
  intercept = TRUE,
  invalid = TRUE,
  d1,
  d2,
  w0,
  M.est = TRUE,
  M = 2,
  bs.Niter = 40,
  bw = NULL
)

Arguments

Y	The outcome observation, a vector of length n.
D	The treatment observation, a vector of length n.
Z	The instrument observation of dimension n \times p_z.
X	The covariates observation of dimension n \times p_x.
intercept	Whether the intercept is included. (default = TRUE)
invalid	If TRUE, the method is robust to the presence of possibly invalid IVs; If FALSE, the method assumes all IVs to be valid. (default = TRUE)
d1	A treatment value for computing CATE(d1,d2|w0).
d2	A treatment value for computing CATE(d1,d2|w0).
w0	A vector of the instruments and baseline covariates for computing CATE(d1,d2|w0).
M.est	If TRUE, M is estimated based on BIC, otherwise M is specified by input value of M. (default = TRUE)
M	The dimension of indices in the outcome model, from 1 to 3. (default = 2)
bs.Niter	The bootstrap resampling size for constructing the confidence interval.
bw	A (M+1) by 1 vector bandwidth specification. (default = NULL)

Value

SpotIV returns an object of class "SpotIV", which "SpotIV" is a list containing the following components:

betaHat	The estimate of the model parameter in front of the treatment.
cateHat	The estimate of CATE(d1,d2|w0).
cate.sdHat	The estimated standard error of cateHat.
SHat	The set of relevant IVs.
VHat	The set of relevant and valid IVs.
Maj.pass	The indicator that the majority rule is satisfied.

References

Li, S., Guo, Z. (2020), Causal Inference for Nonlinear Outcome Models with Possibly Invalid Instrumental Variables, Preprint arXiv:2010.09922.

Examples

data("nonlineardata")
Y <- nonlineardata[,"CVD"]
D <- nonlineardata[,"bmi"]
Z <- as.matrix(nonlineardata[,c("Z.1","Z.2","Z.3","Z.4")])
X <- as.matrix(nonlineardata[,c("age","sex")])
d1 <- median(D)+1
d2 <- median(D)
w0 <- c(rep(0,4), 30, 1)
SpotIV.model <- SpotIV(Y,D,Z,X,invalid = TRUE,d1 =d1, d2 = d2,w0 = w0)
summary(SpotIV.model)

controlfunctionIV documentation built on Dec. 28, 2022, 1:38 a.m.