CADEreg: Regression-based method for the complier average direct...

Description Usage Arguments Details Value Author(s) References

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

1

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.

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 Politics, Princeton University [email protected], http://imai.princeton.edu; Zhichao Jiang, Department of Politics, Princeton University [email protected].

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.


experiment documentation built on May 2, 2019, 9:42 a.m.