CalAPO {RCT2}

Point Estimation and Variance for the unit-level direct effect (ADE), marginal direct effect (MDE), and unit level spillover effect (ASE)

Description

This function calculates the estimated average potential outcomes Y(z,a), point estimates for the ADE, MDE, and ASE, and conservative covariance matrix estimates.

Usage

CalAPO(data)

Arguments

data

A data frame containing the relevant variables. The names for the variables should be “Z” for the treatment assignment, “Y” for the treatment 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 CalAPO which contains the following items:

Y.hat

Estimate of the average potential outcomes.

ADE.est

Estimate of the unit level direct effect.

MDE.est

Estimate of the marginal direct effect.

ASE.est

Estimate of the unti level spillover effect.

cov.hat

Conservative covariance matrix for the estimated potential outcomes.

var.hat.ADE

Estimated variance of the ADE.

var.hat.MDE

Estimated variance of the MDE.

var.hat.ASE

Estimated variance of the ASE.

Author(s)

Kosuke Imai, Department of Statistics, Harvard University [e-mail], https://imai.fas.harvard.edu/; Zhichao Jiang, School of Public Health and Health Sciences, University of Massachusetts Amherst [e-mail]; Karissa Huang, Department of Statistics, Harvard College [e-mail]

References

Zhichao Jiang, Kosuke Imai (2020). “Statistical Inference and Power Analysis for Direct and Spillover Effects in Two-Stage Randomized Experiments”, Technical Report.

Examples

data(jd)
data_LTFC <- data.frame(jd$assigned, jd$pct0, jd$cdd6m, jd$anonale)
colnames(data_LTFC) <- c("Z", "A", "Y", "id")
test <- CalAPO(data_LTFC)
print(CalAPO(data_LTFC))