| mat2targets | R Documentation |
In a data set with n measurements of p variables, intervened
variables can be specified in two ways:
with a logical intervention matrix of dimension
n \times p, where the entry [i, j] indicates whether
variable j has been intervened in measurement i; or
with a list of (unique) intervention targets and a
p-dimensional vector indicating the indices of the intervention
targets of the p measurements.
The function mat2targets converts the first representation to the
second one, the function targets2mat does the reverse conversion. The
second representation can be used to create scoring objects (see
Score) and to run causal inference methods based on
interventional data such as gies or simy.
mat2targets(A)
targets2mat(p, targets, target.index)
A |
Logical matrix with |
p |
Number of variables |
targets |
List of unique intervention targets |
target.index |
Vector of intervention target indices. The intervention
target of data point |
mat2targets returns a list with two components:
targets |
A list of unique intervention targets. |
target.index |
A vector of intervention target indices. The intervention
target of data point |
Alain Hauser (alain.hauser@bfh.ch)
Score, gies, simy
## Specify interventions using a matrix
p <- 5
n <- 10
A <- matrix(FALSE, nrow = n, ncol = p)
for (i in 1:n) A[i, (i-1) %% p + 1] <- TRUE
## Generate list of intervention targets and corresponding indices
target.list <- mat2targets(A)
for (i in 1:length(target.list$target.index))
sprintf("Intervention target of %d-th data point: %d",
i, target.list$targets[[target.list$target.index[i]]])
## Convert back to matrix representation
all(A == targets2mat(p, target.list$targets, target.list$target.index))
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