Description Usage Arguments Value Author(s) References

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.

1 | ```
ATEnocov(Y, Z, data = parent.frame(), match = NULL)
``` |

`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. |

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. |

Kosuke Imai, Department of Politics, Princeton University [email protected], http://imai.princeton.edu;

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.

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