SVMMatch-package: Title: Causal effect estimation and diagnostics with support...
In SVMMatch: Causal Effect Estimation and Diagnostics with Support Vector Machines
Description
Details
Author(s)
References
Description
SVMMatch identifies the region of common support between a set of treated and control units in observational data. Using the observations in this region, a set of balancing weights and a treatment effect are estimated. The method, described in Ratkovic (2014), adapts the support vector machine technology in order to estimate these balancing weights, using a Bayesian implementation so as to give uncertainty effects both in treatment assignment and effect estimation.
Details
Package: SVMMatch
Type: Package
Version: 1.0
Date: 2015-02-05
License: GPL (>= 2)
The method implements the matching algorithm through the main function, svmmatch.
A series of diagnostics are implemented. The function balance() assesses the posterior density of covariate imbalance; effect() returns the posterior estimate of the treatment effect; sensitivity() assesses the effect estimate's sensitivity to unobserved confounders; control.overlap() returns the posterior density of number of control observations returned in matching; and treatment.overlap() examines difficult-to-match treated observations.
Author(s)
Marc Ratkovic
Maintainer: Marc Ratkovic <ratkovic@princeton.edu>
References
Ratkovic, Marc. 2014. "Balancing within the Margin: Causal Effect Estimation with
Support Vector Machines." Working paper.
SVMMatch documentation built on May 2, 2019, 6:34 a.m.
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Description Details Author(s) References
Description
SVMMatch identifies the region of common support between a set of treated and control units in observational data. Using the observations in this region, a set of balancing weights and a treatment effect are estimated. The method, described in Ratkovic (2014), adapts the support vector machine technology in order to estimate these balancing weights, using a Bayesian implementation so as to give uncertainty effects both in treatment assignment and effect estimation.
Details
| Package: | SVMMatch |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2015-02-05 |
| License: | GPL (>= 2) |
The method implements the matching algorithm through the main function, svmmatch.
A series of diagnostics are implemented. The function balance() assesses the posterior density of covariate imbalance; effect() returns the posterior estimate of the treatment effect; sensitivity() assesses the effect estimate's sensitivity to unobserved confounders; control.overlap() returns the posterior density of number of control observations returned in matching; and treatment.overlap() examines difficult-to-match treated observations.
Author(s)
Marc Ratkovic
Maintainer: Marc Ratkovic <ratkovic@princeton.edu>
References
Ratkovic, Marc. 2014. "Balancing within the Margin: Causal Effect Estimation with Support Vector Machines." Working paper.
R Package Documentation
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