IBDInfer {IBDInfer}R Documentation

Design-based Inference for Incomplete Block Designs

Description

Conduct the design-based inference for incomplete block designs.

Usage

IBDInfer(y, b, z, g, w = c("Unit", "Block"), alpha = 0.05, data = NULL)

Arguments

y

Observed outcomes.

b

Block identifier (ID).

z

Assigned treatments.

g

A contrast vector, must sum to zero.

w

A weight vector, must sum to one and contain non-negative values.

alpha

Confidence level, default set to 0.05.

data

A data frame; if provided, y, b, and z should be column names in the data frame.

Value

IBDInfer returns an object of class "IBD", which is a list containing the following components: :

tau.ht

The Horvitz-Thompson estimator of tau.

tau.haj

The Hajek estimator of tau.

var_tau_ht_bb

Variance estimator for the Horvitz-Thompson estimator with between-block bias.

var_tau_ht_wb

Variance estimator for the Horvitz-Thompson estimator with within-block bias.

var_tau_haj_bb

Variance estimator for the Hajek estimator with between-block bias.

var_tau_haj_wb

Variance estimator for the Hajek estimator with within-block bias.

CI_ht_bb

Confidence interval with the Horvitz-Thompson estimator and variance estimator with between-block bias.

CI_ht_wb

Confidence interval with the Horvitz-Thompson estimator and variance estimator with within-block bias.

CI_haj_bb

Confidence interval with the Hajek estimator and variance estimator with between-block bias.

CI_haj_wb

Confidence interval with the Hajek estimator and variance estimator with within-block bias.

yht

The Horvitz-Thompson estimator for each treatment.

yhaj

The Hajek estimator for each treatment.

Sht_bb

Covariance estimator for the Horvitz-Thompson estimator for each treatment with between-block bias.

Sht_wb

Covariance estimator for the Horvitz-Thompson estimator for each treatment with within-block bias.

Shaj_bb

Covariance estimator for the Hajek estimator for each treatment with between-block bias.

Shaj_wb

Covariance estimator for the Hajek estimator for each treatment with within-block bias.

alpha

Confidence level

References

Koo, T., Pashley, N.E. (2024), Design-based Causal Inference for Incomplete Block Designs, arXiv preprint arXiv:2405.19312.

Examples

K <- 6
n.trt <- 3
t <- 2
n.vec <- rep(4, K)
df <- IBDgen(K = K, n.trt = n.trt, t = t, n.vec = n.vec)$blk_assign
df$y <- rnorm(nrow(df), 0, 1)
IBDInfer <- IBDInfer(y = y, b = blk_id, z = assign, g = c(1, -1, 0), w = "Block", data = df)


[Package IBDInfer version 0.0.1 Index]