att {cem} | R Documentation |
An example of ATT estimation from CEM output
att(obj, formula, data, model="linear", extrapolate=FALSE, ntree=2000) ## S3 method for class 'cem.att': plot(x, obj, data, vars=NULL,...)
obj |
a cem.atch or cem.match.list object |
formula |
a model formula. See Details. |
data |
a single data.frame or a list of data.frame's in case of cem.match.list |
model |
one model. See Details. |
extrapolate |
extrapolate the CEM restriced estimate to the whole data. Default = FALSE . |
ntree |
number of trees to generate in random forest model. Default = 2000 . |
x |
the output from the att function |
vars |
a vector of variable names to be used in the parallel plots. By default all variables involved in data matching are used. |
... |
passed to the plot function. |
Argument model
can be lm, linear
for linear regression
model; logit
for the the logistic model;
lme, linear-RE
for the linear model with random effects.
Also rf, forest
for the randomforest algorithm.
If the outcome is y
and the
treatment variable is T
, then a formula
like y ~ T
will produce the simplest estimate the ATT: with lm, it is just the
coefficient on T
, which is the same as the difference in means,
weighted by CEM stratum size. Users can add covariates to span any
remaining imbalance after the match, such as y ~ T + age + sex
,
to adjust for variables age
and sex
.
In the case of multiply imputed datasets, the model is applied to each single matched data and the ATT and is the standard error estimated using the standard formulas for combining results of multiply imputed data.
When extrapolate
= TRUE
, the estimate model is extrapolated
to the whole set of data.
There is a print
method for the output of att
. Specifying the
option TRUE
in a print
command gives complete output from the
estimated model when availalble.
A matrix of estimates with their standard error, or a list in
the case of cem.match.list
.
Stefano Iacus, Gary King, and Giuseppe Porro
Stefano Iacus, Gary King, Giuseppe Porro, ``Matching for Casual Inference Without Balance Checking,'' http://gking.harvard.edu/files/abs/cem-abs.shtml
data(LL) # cem match: automatic bin choice mat <- cem(treatment="treated",data=LL, drop="re78") mat mat$k2k # ATT estimate homo1 <- att(mat, re78~treated, data=LL) rand1 <- att(mat, re78~treated, data=LL, model="linear-RE") rf1 <- att(mat, re78~treated, data=LL, model="rf") homo2 <- att(mat, re78~treated, data=LL, extra=TRUE) rand2 <- att(mat, re78~treated, data=LL, model="linear-RE", extra=TRUE) rf2 <- att(mat, re78~treated, data=LL, model="rf", extra=TRUE) homo1 rand1 rf1 homo2 rand2 rf2 plot( homo1, mat, LL, vars=c("age","education","re74","re75")) plot( rand1, mat, LL, vars=c("age","education","re74","re75")) plot( rf1, mat, LL, vars=c("age","education","re74","re75")) plot( homo2, mat, LL, vars=c("age","education","re74","re75")) plot( rand2, mat, LL, vars=c("age","education","re74","re75")) plot( rf2, mat, LL, vars=c("age","education","re74","re75")) # reduce the match into k2k using euclidean distance within cem strata mat2 <- k2k(mat, LL, "euclidean", 1) mat2 mat2$k2k # ATT estimate after k2k att(mat2, re78~treated, data=LL) # example with missing data # using multiply imputated data # we use Amelia for multiple imputation if(require(Amelia)){ data(LL) n <- dim(LL)[1] k <- dim(LL)[2] # we generate missing values in 30 # randomly in one colum per row LL1 <- LL idx <- sample(1:n, .3*n) invisible(sapply(idx, function(x) LL1[x,sample(2:k,1)] <<- NA)) imputed <- amelia(LL1)[1:5] mat <- cem("treated", datalist=imputed, data=LL1, drop="re78") print(mat) att(mat, re78 ~ treated, data=imputed) }