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 `igraph` object describing the directed acyclic graph induced by the causal model that matches the internal syntax. `expr` A logical value. If `TRUE`, a string is returned describing the expression in LaTeX syntax. Else, a list structure is returned which can be manually parsed by the function `get.expression` `steps` A logical value. If `TRUE`, returns a list where the first element corresponds to the expression of the transport formula and the second to the a list describing intermediary steps taken by the algorithm. `primes` A logical value. If `TRUE`, prime symbols are appended to summation variables to make them distinct from their other instantiations. `stop_on_nonid` A logical value. If `TRUE`, an error is produced when a non-identifiable effect is discovered. Otherwise recursion continues normally.

### 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.

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.

`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)
```

[Package causaleffect version 1.3.13 Index]