svmmatch: SVMMatch for Causal Effect Estimation
In SVMMatch: Causal Effect Estimation and Diagnostics with Support Vector Machines

Description Usage Arguments Details Value Author(s) References See Also Examples

svmmatch estimates balancing weights in the presence of non-random treatment assignment. The method allows the researcher to assess the two crucial ignorability assumptions: a test of sensitivity to omitted confounders and a test of common support between treated and control. A fully Bayesian implementation allows characterization of the posterior of the treatment effect. The method is implemented in C++ through Rcpp, greatly speeding calculation time.

1	svmmatch(treat, X, burnin = 100, gibbs = 200, thin = 2, dv = NULL)

`treat`	A binary vector indicating treatment status.
`X`	A covariate matrix.
`burnin`	Number of burnin draws for the Gibbs sampler.
`gibbs`	Number of posterior draws to be saved.
`thin`	Number of chains when thinning. If burnin = 10, gibbs = 20, and thin=30,then 500 = 10 x 30 + 20 x 30 samples will be drawn, but only 20 returned.
`dv`	A dependent variable of interest. Optional.

Fits SVMMatch to the data, returning balancing weights and a posterior density of the estimated effect.

`effect`	Posterior density of treatment effect on dv.
`beta`	Matrix of posterior draws for coefficients.
`margin`	Binary vector for posterior estimate of marginal observations.
`bal.wts`	Balancing weights.
`X.scale`	Scaled covariate matrix. Used internally.
`X.orig`	Original matrix of covariates.
`treat`	Treatment vector.
`dv`	The dependent variable.

Marc Ratkovic

Ratkovic, Marc. 2014. "Balancing within the Margin: Causal Effect Estimation with Support Vector Machines." Working paper.

balance, effect, sensitivity, control.overlap, treatment.overlap, LaLonde

##Load data
	data("LaLonde")
	Data1<-LaLonde
	Data1<-Data1[Data1$exper==0|Data1$treat==1,]
	attach(Data1)	

##Format X matrix
	varnames<-c("age","educ","black","married","nodegr","hisp",
		"re75","re74")
	X<-cbind(Data1[,varnames],Data1$re75==0,Data1$re74==0)
	X<-as.matrix(X)

##Fit model
	set.seed(1)
	m1.param<-svmmatch(treat, X, dv=re78, burnin=100, gibbs=100, thin=5)

##Assess balance
	bal1<-balance(treat, X, m1.param)

##Summarize treatment effect
	effect1<-effect(m1.param)

## Not run: 
##Sensitivity analysis (Takes a little longer)
	sens1<-sensitivity(m1.param)
	
## End(Not run)

##Assess ignorability:	
	#Assess control overlap--are there enough
	#controls to balance the treated?
		cont1<-control.overlap(m1.param)	

	#Assess treatment overlap--are there treated 
	#values that can't be matched?
		treat1<-treatment.overlap(m1.param)

##Assess autocorrelation
	ac1<-autocorr(m1.param)