# Find Instrumental Variables

### Description

Generates a list of instrumental variables that can be used to infer the total effect of an exposure on an outcome in the presence of latent confounding, under linearity assumptions.

### Usage

1 | ```
instrumentalVariables(x, exposure = NULL, outcome = NULL)
``` |

### Arguments

`x` |
the input graph, a DAG. |

`exposure` |
name of the exposure variable. If not given (default), then the exposure variable is supposed to be defined in the graph itself. Only a single exposure variable and a single outcome variable supported. |

`outcome` |
name of the outcome variable, also taken from the graph if not given. Only a single outcome variable is supported. |

### References

B. van der Zander, J. Textor and M. Liskiewicz (2015),
Efficiently Finding Conditional Instruments for Causal Inference.
In *Proceedings of the 24th International Joint Conference on
Artificial Intelligence (IJCAI 2015)*, pp. 3243-3249. AAAI Press, 2015.

### Examples

1 2 3 4 | ```
# The classic IV model
instrumentalVariables( "dag{ i->x->y; x<->y }", "x", "y" )
# A conditional instrumental variable
instrumentalVariables( "dag{ i->x->y; x<->y ; y<-z->i }", "x", "y" )
``` |