CalAPO: Point Estimation and Variance for the unit-level direct...

In RCT2: Designing and Analyzing Two-Stage Randomized Experiments

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))

RCT2 documentation built on Oct. 18, 2022, 9:09 a.m.