transport {causaleffect} | R Documentation |

## Derive a transport formula for a causal effect between two domains

### Description

This function returns an expression for the transport formula of a causal effect between two domains. The formula is returned for the interventional distribution of the set of variables (`y`

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

) in a selection diagram (`D`

). If the effect is non-transportable, an error is thrown describing the graphical structure that witnesses non-transportability. The vertices of (`D`

) that correspond to selection variables must have a description parameter of a single character "S" (shorthand for "selection"). By default, every variable is available for intervention in the source. If only a subset of the variables is available, then the set (`z`

) can be used to derive specific z-transportability.

### Usage

```
transport(y, x, z = NULL, D, 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 of variables available for intervention. NULL value corresponds to ordinary transportability. |

`D` |
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 transport formula. Otherwise, a list as described in the arguments.

### Author(s)

Santtu Tikka

### References

Bareinboim E., Pearl J. 2013a A General Algorithm for Deciding Transportability of Experimental Results. *Journal of Causal Inference*, **1**, 107–134.

Bareinboim E., Pearl J. 2013c Causal Transportability with Limited Experiments. *Proceedings of the 27th AAAI Conference on Artificial Intelligence*, 95–101.

### See Also

`parse.graphml`

, `get.expression`

, `generalize`

, `meta.transport`

### Examples

```
library(igraph)
# We set simplify = FALSE to allow multiple edges.
d <- graph.formula(X -+ Z, Z -+ W, W -+ V, V -+ Y, S -+ V, # Observed edges
X -+ Z, Z -+ X, V -+ Y, Y -+ V, X -+ Y, Y -+ X, simplify = FALSE)
# Here the bidirected edges are set to be unobserved in the selection diagram d.
# This is denoted by giving them a description attribute with the value "U".
# The first five edges are observed, the rest are unobserved.
d <- set.edge.attribute(d, "description", 6:11, "U")
# The variable "S" is a selection variable. This is denoted by giving it
# a description attribute with the value "S".
d <- set.vertex.attribute(d, "description", 6, "S")
transport(y = "Y", x = "X", D = d)
```

*causaleffect*version 1.3.15 Index]