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

`sensitivity`

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

1 2 3 4 | ```
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")
``` |

`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 |

`label.main` |
Main title for figure |

`label.x` |
X-axis label. |

`label.y` |
Y-axis label. |

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.

`sens.mat` |
A matrix of the posterior estimates as a function of the unobserved confounder. |

Marc Ratkovic

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

svmmatch, legend

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
## 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)
``` |

```
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
```

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