Description Usage Arguments Value Author(s) References
This function estimates various average treatment effect in clusterrandomized experiments without using pretreatment covariates. The treatment variable is assumed to be binary. Currently, only the matchedpair design is allowed. The details of the methods for this design are given in Imai, King, and Nall (2007).
1 2  ATEcluster(Y, Z, grp, data = parent.frame(), match = NULL, weights = NULL,
fpc = TRUE)

Y 
The outcome variable of interest. 
Z 
The (randomized) clusterlevel treatment variable. This variable should be binary. Two units in the same cluster should have the same value. 
grp 
A variable indicating clusters of units. Two units in the same cluster should have the same value. 
data 
A data frame containing the relevant variables. 
match 
A variable indicating matchedpairs of clusters. Two units in
the same matchedpair of clusters should have the same value. The default is

weights 
A variable indicating the population cluster sizes, which
will be used to construct weights for each pair of clusters. Two units in
the same cluster should have the same value. The default is 
fpc 
A logical variable indicating whether or not finite population
correction should be used for estimating the lower bound of CACE variance.
This is relevant only when 
A list of class ATEcluster
which contains the following
items:
call 
The matched call. 
n 
The total number of units. 
n1 
The total number of units in the treatment group. 
n0 
The total number of units in the control group. 
Y 
The outcome variable. 
Y1bar 
The clusterspecific (unweighted) average value of the observed outcome for the treatment group. 
Y0bar 
The clusterspecific (unweighted) average value of the observed outcome for the treatment group. 
Y1var 
The clusterspecific sample variance of the observed outcome for the treatment group. 
Y0var 
The clusterspecific sample variance of the observed outcome for the control group. 
Z 
The treatment variable. 
grp 
The clusterindicator variable. 
match 
The matchedpair indicator variable. 
weights 
The weight variable in its original form. 
est 
The estimated average treatment effect based on the arithmetic mean weights. 
var 
The estimated variance of the average treatment effect estimator based on the arithmetic mean weights. This uses the variance formula provided in Imai, King, and Nall (2007). 
var.lb 
The estimated sharp lower bound of the cluster average treatment effect estimator using the arithmetic mean weights. 
est.dk 
The estimated average treatment effect based on the harmonic mean weights. 
var.dk 
The estimated variance of the average treatment effect estimator based on the harmonic mean weights. This uses the variance formula provided in Donner and Klar (1993). 
dkvar 
The estimated variance of the average treatment effect estimator based on the harmonic mean weights. This uses the variance formula provided in Imai, King, and Nall (2007). 
eff 
The estimated relative efficiency of the matchedpair design over the completely randomized design (the ratio of two estimated variances). 
m 
The number of pairs in the matchedpair design. 
N1 
The population cluster sizes for the treatment group. 
N0 
The population cluster sizes for the control group. 
w1 
Clusterspecific weights for the treatment group. 
w0 
Clusterspecific weights for the control group. 
w 
Pairspecific
normalized arithmetic mean weights. These weights sum up to the total number
of units in the sample, i.e., 
w.dk 
Pairspecific
normalized harmonic mean weights. These weights sum up to the total number
of units in the sample, i.e., 
diff 
Withinpair
differences if the matchedpair design is analyzed. This equals the
difference between 
Kosuke Imai, Department of Politics, Princeton University [email protected], http://imai.princeton.edu;
Donner, A. and N. Klar (1993). “Confidence interval construction for effect measures arising from cluster randomized trials.” Journal of Clinical Epidemiology. Vol. 46, No. 2, pp. 123131.
Imai, Kosuke, Gary King, and Clayton Nall (2007). “The Essential Role of Pair Matching in ClusterRandomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation”, Technical Report. Department of Politics, Princeton University.
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