generalize {causaleffect} | R Documentation |

## Derive a transport formula for a causal effect between a target domain and multiple source domains with limited experiments

### Description

This function returns an expression for the transport formula of a causal effect between a target domain and multiple source domains with limited experiments. The formula is returned for the interventional distribution of the set of variables (`y`

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

). Available experiments are depicted by a list (`Z`

) where the first element describes the elements available at the target and the rest at the sources. The multiple domains are given as a list (`D`

) where the first element is the underlying causal diagram without selection variables, and the rest correspond to the selection diagrams. If the effect is non-transportable, an error is thrown describing the graphical structure that witnesses non-transportability. The vertices of any diagram in (`D`

) that correspond to selection variables must have a description parameter of a single character "S" (shorthand for "selection").

### Usage

```
generalize(y, x, Z, D, expr = TRUE, simp = FALSE,
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 list of character vectors describing the available interventions at each domain. |

`D` |
A list of |

`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. 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

`aux.effect`

, `causal.effect`

, `get.expression`

, `meta.transport`

, `parse.graphml`

, `recover`

, `transport`

### Examples

```
library(igraph)
# Selection diagram corresponding to the target domain (no selection variables).
# We set simplify = FALSE to allow multiple edges.
d1 <- graph.formula(Z_1 -+ X, Z_2 -+ X, X -+ Z_3, Z_3 -+ W,
Z_3 -+ U, U -+ Y, W -+ U, Z_1 -+ Z_3, # Observed edges
Z_1 -+ Z_2, Z_2 -+ Z_1, Z_1 -+ X, X -+ Z_1,
Z_2 -+ Z_3, Z_3 -+ Z_2, Z_2 -+ U, U -+ Z_2,
W -+ Y, Y -+ W, simplify = FALSE)
# Here the bidirected edges are set to be unobserved in the selection diagram d1.
# This is denoted by giving them a description attribute with the value "U".
# The first 8 edges are observed and the next 10 are unobserved.
d1 <- set.edge.attribute(d1, "description", 9:18, "U")
# We can use the causal diagram d1 to create selection diagrams
# for two source domains, a and b.
d1a <- union(d1, graph.formula(S_1 -+ Z_2, S_2 -+ Z_3, S_3 -+ W))
# The variables "S_1", "S_2", and "S_3" are selection variables.
# This is denoted by giving them a description attribute with the value "S".
# The graph already has 7 vertices, so the last three depict the new ones.
d1a <- set.vertex.attribute(d1a, "description", 8:10, "S")
# Selection diagram corresponding to the second
# source domain is constructed in a similar fashion.
d1b <- union(d1, graph.formula(S_1 -+ Z_1, S_2 -+ W, S_3 -+ U))
d1b <- set.vertex.attribute(d1b, "description", 8:10, "S")
# We combine the diagrams as a list.
d.comb <- list(d1, d1a, d1b)
# We still need the available experiments at each domain.
z <- list(c("Z_1"), c("Z_2"), c("Z_1"))
# This denotes that the variable "Z_1" is available for intervention
# in both the target domain, and the second source domain.
# The variable "Z_2" is available for intervention in the first source domain.
generalize(y = "Y", x = "X", Z = z, D = d.comb)
```

*causaleffect*version 1.3.15 Index]