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.
ATEnocov(Y, Z, data = parent.frame(), match = NULL)The outcome variable of interest.
The (randomized) treatment variable. This variable should be binary.
A data frame containing the relevant variables.
A variable indicating matched-pairs. The two units in the same matched-pair should have the same value.
A list of class ATEnocov which contains the following items:
The matched call.
The outcome variable.
The treatment variable.
The matched-pair indicator variable.
The estimated average treatment effect.
The estimated variance of the average treatment effect estimator.
Within-pair differences if the matched-pair design is analyzed.
Imai, Kosuke, (2008). “Randomization-based Inference and Efficiency Analysis in Experiments under the Matched-Pair Design”, Statistics in Medicine.