CADEparamreg {RCT2}

Regression-based method for the ITT effects and the complier average direct effect/spillover effect

Description

This function computes the point estimates and variance estimates of the direct effect and spillover effect for ITT and CADE/CASE

Usage

CADEparamreg(data, assign.prob, 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.

assign.prob

A double between 0 and 1 specifying the assignment probability to either assignment mechanism.

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 CADEparamreg which contains the following items:

ITT.DE

Estimate of direct effect under ITT regresion.

ITT.SE

Estimate of spillover effect under ITT regresion.

ITT.DE.CI

Confidence itnerval of direct effect under ITT regresion.

ITT.SE.CI

Confidence itnerval of spillover effect under ITT regresion.

IV.DE

Estimate of direct effect under IV regresion.

IV.SE

Estimate of spillover effect under IV regresion.

IV.DE.CI

Confidence interval of direct effect under IV regresion.

IV.SE.CI

Confidence interval of spillover effect under IV regresion.

IV.DE.CI

Confidence interval of direct effect under IV regresion.

ITT.tstat

t-stats from ITT regression.

IV.tstat

t-stats from IV regression.

ITT.pvals

p-values from ITT regression.

IV.pvals

p-values from IV regression.

data(india) india$id <- factor(india$id) CADEreg(india, ci.level = 0.90)

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

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.