Horvitz-Thompson estimator for RCTs, with Joel Middleton
R
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
R
man
notes
tests
.Rbuildignore
.gitignore
DESCRIPTION
NAMESPACE
README.md
TODO

README.md

htestimate

Htestimate calculates unbiased estimates of treatment effects from randomized trials when the random assignment is correlated across units, using the Horvitz-Thompson estimator (Särndal et al. 2003, section 2.8). Standard approaches to RCT evaluation (difference in means and regression) are generally biased under clustered randomization (Middleton 2008) or under rerandomization (Morgan & Rubin 2012), for example. In addition to the treatment effect the package produces a standard error and p-value of that effect estimate. Differences in outcome totals rather than means can also be produced. Any number of experimental arms/conditions are allowed.

This package is currently under active development so bug reports and feature requests are encouraged.

Install

Install directly from github using devtools:

install.packages("devtools")     # If not already installed.
devtools::install_github("ck37/htestimate")
library(htestimate)

Requirements

R packages: dplyr

Examples

# Example using data from RI package.
y <- c(8,6,2,0,3,1,1,1,2,2,0,1,0,2,2,4,1,1)
Z <- c(1,1,0,0,1,1,0,0,1,1,1,1,0,0,1,1,0,0)
# Generate 10,000 random permutations of the assignment vector.
perms = ri::genperms(Z, maxiter=10000)
# Estimate the probability of assignment for each unit and assignment level.
prob_matrix = createProbMatrix(perms)
# Estimate the treatment effect using Horvitz-Thompson.
htestimate(y, Z, contrasts = c(-1, 1), prob_matrix = prob_matrix)

References

Aronow, P. M., & Middleton, J. A. (2013). A class of unbiased estimators of the average treatment effect in randomized experiments. Journal of Causal Inference, 1(1), 135-154.

Middleton, J. A. (2008). Bias of the regression estimator for experiments using clustered random assignment. Statistics & Probability Letters, 78(16), 2654-2659.

Morgan, K. L., & Rubin, D. B. (2012). Rerandomization to improve covariate balance in experiments. The Annals of Statistics, 40(2), 1263-1282.

Särndal, C. E., Swensson, B., & Wretman, J. (2003). Model assisted survey sampling. Springer Science & Business Media.