SVMMatch: Causal Effect Estimation and Diagnostics with Support Vector Machines

Causal effect estimation in observational data often requires identifying a set of untreated observations that are comparable to some treated group of interest. This package provides a suite of functions for identifying such a set of observations and for implementing standard and new diagnostics tools. The primary function, svmmatch(), uses support vector machines to identify a region of common support between treatment and control groups. A sensitivity analysis, balance checking, and assessment of the region of overlap between treated and control groups is included. The Bayesian implementation allows for recovery of uncertainty estimates for the treatment effect and all other parameters.

AuthorMarc Ratkovic
Date of publication2015-02-08 09:24:56
MaintainerMarc Ratkovic <ratkovic@princeton.edu>
LicenseGPL (>= 2)
Version1.1

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Files in this package

SVMMatch
SVMMatch/src
SVMMatch/src/Makevars
SVMMatch/src/Makevars.win
SVMMatch/src/RcppExports.cpp
SVMMatch/src/rcpp_bayesmatch.cpp
SVMMatch/NAMESPACE
SVMMatch/NEWS
SVMMatch/data
SVMMatch/data/LaLonde.tab.gz
SVMMatch/R
SVMMatch/R/RcppExports.R SVMMatch/R/SVMFunctions.R
SVMMatch/MD5
SVMMatch/DESCRIPTION
SVMMatch/man
SVMMatch/man/control.overlap.Rd SVMMatch/man/sensitivity.Rd SVMMatch/man/bayesmatch_cpp.Rd SVMMatch/man/SVMMatch-package.Rd SVMMatch/man/autocorr.Rd SVMMatch/man/svmmatch.Rd SVMMatch/man/treatment.overlap.Rd SVMMatch/man/LaLonde.Rd SVMMatch/man/balance.Rd SVMMatch/man/effect.Rd

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