surrogate.outcome {causaleffect} | R Documentation |

## Derive a formula for a causal effect using surrogate outcomes

### Description

This function returns an expression for the causal effect of interest using surrogate outcomes. The formula is returned for the interventional distribution of the set of variables (`y`

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

). Available experimental data are depicted by a list (`S`

) where each element is a list with two elements, `Z`

and `W`

, that are character vectors describing the experiments and the outcome variables, respectively.

### Usage

```
surrogate.outcome(y, x, S, G, expr = 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. |

`S` |
A list describing the available experimental data. |

`G` |
An |

`expr` |
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 causal effect. Otherwise, a list as described in the arguments.

### Author(s)

Santtu Tikka

### References

Bareinboim E., Pearl J. 2014 Transportability from Multiple Environments with Limited Experiments: Completeness Results. *Proceedings of the 27th Annual Conference on Neural Information Processing Systems*, 280–288.

### See Also

`generalize`

, `causal.effect`

, `get.expression`

### Examples

```
library(igraph)
# We set simplify = FALSE to allow multiple edges.
g <- graph.formula(W -+ X, W -+ Z, X -+ Z, Z -+ Y, # Observed edges
X -+ Z, Z -+ X, simplify = FALSE)
# We set the bidirected edges
g <- set.edge.attribute(g, "description", 5:6, "U")
# We construct the set of available experimental data
s <- list(
list(Z = c("X"), W = c("Z"))
)
surrogate.outcome(y = "Y", x = "X", S = s, G = g)
```

*causaleffect*version 1.3.15 Index]