aux.effect {causaleffect} | R Documentation |

## Identify a causal effect using surrogate experiments

### Description

This function returns an expression for the joint distribution of the set of variables (`y`

)
given the intervention on the set of variables (`x`

) using auxiliary experiments on a set (`z`

) if the effect is identifiable. Otherwise
an error is thrown describing the graphical structure that witnesses non-identifiability.

### Usage

```
aux.effect(y, x, z, G, expr = TRUE, simp = TRUE,
steps = FALSE, primes = FALSE, stop_on_nonid = TRUE)
```

### Arguments

`y` |
A character vector of variables of interest given the intervention. |

`x` |
A character vector of the variables that are acted upon. |

`z` |
A character vector describing the additional set available for manipulation. |

`G` |
An |

`expr` |
A logical value. If |

`simp` |
A logical value. If |

`steps` |
A logical value. If |

`primes` |
A logical value. If |

`stop_on_nonid` |
A logical value. If |

### Value

If `steps = FALSE`

, A character string or an object of class `probability`

that describes the interventional distribution. Otherwise, a list as described in the arguments.

### Author(s)

Santtu Tikka

### References

Bareinboim E., Pearl J. 2012 Causal Inference by Surrogate Experiments: z-identifiability. *Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence*, 113–120.

### See Also

### Examples

```
library(igraph)
# simplify = FALSE to allow multiple edges
f <- graph.formula(W -+ Z, Z -+ X, X -+ Y, W -+ Y, # Observed edges
W -+ Y, Y -+ W, Z -+ Y, Y -+ Z, Z -+ X, X -+ Z, simplify = FALSE)
# Here the bidirected edges are set to be unobserved in graph g
# This is denoted by giving them a description attribute with the value "U"
# The first 4 edges correspond to the observed edges, the rest are unobserved
f <- set.edge.attribute(f, "description", 5:10, "U")
aux.effect(y = "Y", x = "X", z = "Z", G = f)
```

*causaleffect*version 1.3.15 Index]