ce_estimate_vm_att {CIMTx}

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