ATEnocov: Estimation of the Average Treatment Effect in Randomized...

Description Usage Arguments Value Author(s) References

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

1

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 [email protected], 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.


experiment documentation built on May 2, 2019, 9:42 a.m.