Description Usage Arguments Value Author(s) References
This function estimates various complier average causal effect in clusterrandomized experiments without using pretreatment covariates when unitlevel noncompliance exists. Both the encouragement and treatment variables are 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  CACEcluster(Y, D, Z, grp, data = parent.frame(), match = NULL,
weights = NULL, ...)

Y 
The outcome variable of interest. 
D 
The unitlevel treatment receipt variable. This variable should be binary but can differ across units within each cluster. 
Z 
The (randomized) clusterlevel encouragement 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 
... 
Optional arguments passed to 
A list of class CACEcluster
which contains the following
items:
call 
The matched call. 
ITTY 
The output object from

ITTD 
The output object
from 
n1 
The total number of units in the treatment group. 
n0 
The total number of units in the control group. 
Z 
The treatment variable. 
est 
The estimated complier average causal effect. 
var 
The estimated variance of the complier average causal effect estimator. 
cov 
The estimated covariance between two ITT estimator. 
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. 
w 
Pairspecific normalized
arithmetic mean weights. These weights sum up to the total number of units
in the sample, i.e., 
Kosuke Imai, Department of Politics, Princeton University [email protected], http://imai.princeton.edu;
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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