ce_estimate_vm_att: Causal inference with multiple treatments using VM for ATT...
In CIMTx: Causal Inference for Multiple Treatments with a Binary Outcome
View source: R/ce_estimate_vm_att.R
ce_estimate_vm_att R Documentation
Causal inference with multiple treatments using VM for ATT effects
Description
The function ce_estimate_vm_att implements
VM to estimate ATT effect with
multiple treatments using observational data.
Usage
ce_estimate_vm_att(y, x, w, reference_trt, caliper, n_cluster)
Arguments
y
A numeric vector (0, 1) representing a binary outcome.
x
A dataframe, including all the covariates but not treatments.
w
A numeric vector representing the treatment groups.
reference_trt
A numeric value indicating reference treatment group
for ATT effect.
caliper
A numeric value denoting the caliper on the logit of
GPS within each cluster formed by K-means clustering.
The caliper is in standardized units.
For example, caliper = 0.25 means that
all matches greater than 0.25 standard deviations of the
logit of GPS are dropped. The default value is 0.25.
n_cluster
A numeric value denoting the number of clusters to
form using K means clustering on the logit of GPS.
Value
A summary of the effect estimates can be obtained
with summary function. The output also contains the number
of matched individuals.
References
Venables, W. N. & Ripley, B. D. (2002)
Modern Applied Statistics with S.
Fourth Edition. Springer, New York. ISBN 0-387-95457-0
Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021).
dplyr: A Grammar of Data Manipulation.
R package version 1.0.7.
URL: https://CRAN.R-project.org/package=dplyr
Jasjeet S. Sekhon (2011).
Multivariate and Propensity Score Matching Software with A
utomated Balance Optimization: The Matching Package for R.
Journal of Statistical Software, 42(7), 1-52
CIMTx documentation built on June 24, 2022, 9:07 a.m.
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View source: R/ce_estimate_vm_att.R
| ce_estimate_vm_att | R Documentation |
Causal inference with multiple treatments using VM for ATT effects
Description
The function ce_estimate_vm_att implements
VM to estimate ATT effect with
multiple treatments using observational data.
Usage
ce_estimate_vm_att(y, x, w, reference_trt, caliper, n_cluster)
Arguments
y |
A numeric vector (0, 1) representing a binary outcome. |
x |
A dataframe, including all the covariates but not treatments. |
w |
A numeric vector representing the treatment groups. |
reference_trt |
A numeric value indicating reference treatment group for ATT effect. |
caliper |
A numeric value denoting the caliper on the logit of
GPS within each cluster formed by K-means clustering.
The caliper is in standardized units.
For example, |
n_cluster |
A numeric value denoting the number of clusters to form using K means clustering on the logit of GPS. |
Value
A summary of the effect estimates can be obtained
with summary function. The output also contains the number
of matched individuals.
References
Venables, W. N. & Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0
Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.7. URL: https://CRAN.R-project.org/package=dplyr
Jasjeet S. Sekhon (2011). Multivariate and Propensity Score Matching Software with A utomated Balance Optimization: The Matching Package for R. Journal of Statistical Software, 42(7), 1-52
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