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.
- Marc Ratkovic
- Date of publication
- 2015-02-08 09:24:56
- Marc Ratkovic <firstname.lastname@example.org>
- GPL (>= 2)
- Autocorrelation in estimated coefficients.
- Assessing balance when using SVMMatch.
- Rcpp implementation for Bayesian SVM.
- Assessing the number of control observations used in...
- Posterior density of the treatment effect estimate from an...
- LaLonde Data for Covariate Balancing Propensity Score
- Sensitivity analysis for SVMMatch.
- SVMMatch for Causal Effect Estimation
- Title: Causal effect estimation and diagnostics with support...
- Exploring hard-to-match treated observations.
Files in this package