SVMMatch (version 1.1)

effect: Posterior density of the treatment effect estimate from an SVMMatch object.

Description

effect characterizes the posterior density of the treatment effect, as estimated through SVMMatch.

Usage

effect(obj, color=TRUE, quant=c(0.025,0.975), legend.pos="topleft",
		label.main="Posterior Density of Effect Estimate", label.x="Outcome",
		label.y="Density")

Arguments

obj

A fitted SVMMatch object.

color

Whether to plot in color or black and white. TRUE or FALSE.

quant

Quantiles of the effect to be marked on the figure.

legend.pos

Where to place the margin. See the help file for legend.

label.main

Main title for figure

label.x

X-axis label.

label.y

Y-axis label.

Value

balance

Posterior density of imbalance, by covariate.

Details

Characterizes and plots the posterior density of the treatment effect, given an SVMMatch object. At each posterior draw, a set of balancing weights are constructed, as described in Ratkovic (2014). A treatment effect is estimated for each posterior draw of balancing weights, and the posterior density returned as a figure.

References

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

See Also

svmmatch, legend

Examples

## Not run: 
# ##See svmmatch() for a full implementation
# ##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)
# 
# ##Summarize treatment effect
# 	effect1<-effect(m1.param)
# 
# 	## End(Not run)