sensitivity: Sensitivity analysis for SVMMatch.

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

Description

sensitivity assesses the sensitivity of an effect estimate to an omitted confounder.

Usage

	sensitivity(obj, seq.eval=seq(-1,1,.1), quant.eval=c(0.025,0.5,0.975), 
		color=TRUE, legend.pos="topleft", label.main="Sensitivity Analysis",
		label.x="Sensitivity Parameter",  label.y="Outcome")	
	

Arguments

obj	A fitted SVMMatch object.
seq.eval	Values at which to set the omitted confounder, in the range [-1, +1].
quant.eval	Values at which to plot the posterior density as a function of the omitted confounder. By default, a solid line is drawn through the posterior medians, with dashed lines at the 2.5th and 97.5th percentiles.
color	Whether to plot in color or black and white. TRUE or FALSE.
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.

Details

Conducts a sensitivity analysis using an SVMMatch object. An unoberseved parameter, u, that predicts the treatment assignment is introduced and varied between -1 and 1. For each value of u, balancing weights are constructed and the posterior density of the effect estimate recalcluated, with u=0 returning the results from the original fit. The figure gives the researcher a sense as to how sensitive the effect estimate is to omitted confounders.

Value

sens.mat

A matrix of the posterior estimates as a function of the unobserved confounder.

Author(s)

Marc Ratkovic

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)

##Sensitivity analysis (Takes a little longer)
	sens1<-sensitivity(m1.param)

	
## End(Not run)

Example output

Warning messages:
1: In par(par.old) : graphical parameter "cin" cannot be set
2: In par(par.old) : graphical parameter "cra" cannot be set
3: In par(par.old) : graphical parameter "csi" cannot be set
4: In par(par.old) : graphical parameter "cxy" cannot be set
5: In par(par.old) : graphical parameter "din" cannot be set
6: In par(par.old) : graphical parameter "page" cannot be set

SVMMatch documentation built on May 2, 2019, 6:34 a.m.