| CADEreg {RCT2} | R Documentation |
Regression-based method for the complier average direct effect
Description
This function computes the point estimates of the complier average direct effect (CADE) and four
different variance estimates: the HC2 variance, the cluster-robust variance, the cluster-robust HC2
variance and the variance proposed in the reference. The estimators calculated using this function
are cluster-weighted, i.e., the weights are equal for each cluster. To obtain the indivudal-weighted
estimators, please multiply the recieved treatment and the outcome by n_jJ/N, where
n_j is the number of individuals in cluster j, J is the number of clusters and
N is the total number of individuals.
Usage
CADEreg(data, ci.level = 0.95)
Arguments
data |
A data frame containing the relevant variables. The names for the variables should be: “Z” for the treatment assignment, “D” for the actual received treatment, “Y” for the outcome, “A” for the treatment assignment mechanism and “id” for the cluster ID. The variable for the cluster id should be a factor. |
ci.level |
A double between 0 and 1 specifying the confidence interval level to be output. |
Details
For the details of the method implemented by this function, see the references.
Value
A list of class CADEreg which contains the following items:
CADE1 |
The point estimate of CADE(1). |
CADE0 |
The point estimate of CADE(0). |
var1.clu |
The cluster-robust variance of CADE(1). |
var0.clu |
The cluster-robust variance of CADE(0). |
var1.clu.hc2 |
The cluster-robust HC2 variance of CADE(1). |
var0.clu.hc2 |
The cluster-robust HC2 variance of CADE(0). |
var1.hc2 |
The HC2 variance of CADE(1). |
var0.hc2 |
The HC2 variance of CADE(0). |
var1.ind |
The individual-robust variance of CADE(1). |
var0.ind |
The individual-robust variance of CADE(0). |
var1.reg |
The proposed variance of CADE(1). |
var0.reg |
The proposed variance of CADE(0). |
Author(s)
Kosuke Imai, Department of Statistics, Harvard University imai@harvard.edu, https://imai.fas.harvard.edu/; Zhichao Jiang, School of Public Health and Health Sciences, University of Massachusetts Amherst zhichaojiang@umass.edu; Karissa Huang, Department of Statistics, Harvard College krhuang@college.harvard.edu
References
Kosuke Imai, Zhichao Jiang and Anup Malani (2018). “Causal Inference with Interference and Noncompliance in the Two-Stage Randomized Experiments”, Technical Report. Department of Politics, Princeton University.
Examples
data(india)
india$id <- factor(india$id)
CADEreg(india, ci.level = 0.90)