ATEnocov {experiment}R Documentation

Estimation of the Average Treatment Effect in Randomized Experiments

Description

This function computes the standard ``difference-in-means'' estimate of the average treatment effect in randomized experiments without using pre-treatment covariates. The treatment variable is assumed to be binary. Currently, the two designs are allowed: complete randomized design and matched-pair design.

Usage

  ATEnocov(Y, Z, data = parent.frame(), match = NULL)

Arguments

Y The outcome variable of interest.
Z The (randomized) treatment variable. This variable should be binary.
data A data frame containing the relevant variables.
match A variable indicating matched-pairs. The two units in the same matched-pair should have the same value.

Value

A list of class ATEnocov which contains the following items:

call The matched call.
Y The outcome variable.
Z The treatment variable.
match The matched-pair indicator variable.
ATEest The estimated average treatment effect.
ATE.var The estimated variance of the average treatment effect estimator.
diff Within-pair differences if the matched-pair design is analyzed.

Author(s)

Kosuke Imai, Department of Politics, Princeton University kimai@Princeton.Edu, http://imai.princeton.edu;

References

Imai, Kosuke, (2007). “Randomization-based Inference and Efficiency Analysis in Experiments under the Matched-Pair Design”, Technical Report. Department of Politics, Princeton University.


[Package experiment version 1.1-0 Index]