CADErand {experiment}

Randomization-based method for the complier average direct effect and the complier average spillover effect

Description

This function computes the point estimates and variance estimates of the complier average direct effect (CADE) and the complier average spillover effect (CASE). The estimators calculated using this function are either individual weighted or cluster-weighted. The point estimates and variances of ITT effects are also included.

Usage

CADErand(data, individual = 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.

individual

A binary variable with TRUE for individual-weighted estimators and FALSE for cluster-weighted estimators.

Details

For the details of the method implemented by this function, see the references.

Value

A list of class CADErand which contains the following items:

CADE1

The point estimate of CADE(1).

CADE0

The point estimate of CADE(0).

CADE1

The point estimate of CASE(1).

CASE0

The point estimate of CASE(0).

var.CADE1

The variance estimate of CADE(1).

var.CADE0

The variance estimate of CADE(0).

var.CASE1

The variance estimate of CASE(1).

var.CASE0

The variance estimate of CASE(0).

DEY1

The point estimate of DEY(1).

DEY0

The point estimate of DEY(0).

DED1

The point estimate of DED(1).

DED0

The point estimate of DED(0).

var.DEY1

The variance estimate of DEY(1).

var.DEY0

The variance estimate of DEY(0).

var.DED1

The variance estimate of DED(1).

var.DED0

The variance estimate of DED(0).

SEY1

The point estimate of SEY(1).

SEY0

The point estimate of SEY(0).

SED1

The point estimate of SED(1).

SED0

The point estimate of SED(0).

var.SEY1

The variance estimate of SEY(1).

var.SEY0

The variance estimate of SEY(0).

var.SED1

The variance estimate of SED(1).

var.SED0

The variance estimate of SED(0).

Author(s)

Kosuke Imai, Department of Government and Department of Statistics, Harvard University [e-mail], https://imai.fas.harvard.edu; Zhichao Jiang, Department of Politics, Princeton University [e-mail].

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.