The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Title: Graph Adjustment Identification Distances for Causal Graphs
Version: 0.1.0
Description: Make efficient Rust implementations of graph adjustment identification distances available in R. These distances (based on ancestor, optimal, and parent adjustment) count how often the respective adjustment identification strategy leads to causal inferences that are incorrect relative to a ground-truth graph when applied to a candidate graph instead. See also Henckel, Würtzen, Weichwald (2024) <doi:10.48550/arXiv.2402.08616>.
License: MPL version 1.0 | MPL version 1.1 | MPL version 2.0 [expanded from: MPL]
Encoding: UTF-8
RoxygenNote: 7.3.2
Config/rextendr/version: 0.4.1
SystemRequirements: Cargo (Rust's package manager), rustc
Depends: R (≥ 4.2)
Suggests: testthat (≥ 3.0.0)
Config/testthat/edition: 3
URL: https://github.com/CausalDisco/gadjid
BugReports: https://github.com/CausalDisco/gadjid/issues
NeedsCompilation: yes
Packaged: 2025-08-25 08:44:52 UTC; qbr527
Author: Sebastian Weichwald [aut, cph, cre], Theo Würtzen [aut], Leonard Henckel [aut], Authors of the dependency Rust crates [ctb] (see inst/AUTHORS file for details)
Maintainer: Sebastian Weichwald <sweichwald@math.ku.dk>
Repository: CRAN
Date/Publication: 2025-08-29 11:00:08 UTC

Ancestor Adjustment Identification Distance between two DAG / CPDAG adjacency matrices

Description

Computes the ancestor adjustment intervention distance between the true g_true DAG or CPDAG and an estimated g_guess DAG or CPDAG.

Usage

ancestor_aid(g_true, g_guess, edge_direction)

Arguments

g_true

Adjacency matrix of the true graph

g_guess

Adjacency matrix of the guess graph

edge_direction

either "from row to column" or "from column to row"

Details

For details see Henckel, Würtzen, Weichwald (2024) doi:10.48550/arXiv.2402.08616
The source code is available at github.com/CausalDisco/gadjid

Graph inputs are accepted as adjacency matrices of type double. An adjacency matrix for a DAG may only contain 0s and 1s. An adjacency matrix for a CPDAG may only contain 0s, 1s and 2s. DAG and CPDAG inputs are validated for acyclicity. However, for CPDAG inputs, the user needs to ensure the adjacency matrix indeed codes a valid CPDAG (instead of just a PDAG).

If edge_direction="from row to column", then a 1 in row r and column c codes a directed edge ‘r → c’; if edge_direction="from column to row", then a 1 in row r and column c codes a directed edge ‘c → r’; for either setting of edge_direction, a 2 in row r and column c codes an undirected edge ‘r – c’ (an additional 2 in row c and column r is ignored; one of the two entries is sufficient to code an undirected edge).

Value

2-element vector of type double
c(normalized error in [0,1], total number of errors)

References

L Henckel, T Würtzen, S Weichwald. "Adjustment Identification Distance: A gadjid for Causal Structure Learning." Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence (UAI), 2024. doi:10.48550/arXiv.2402.08616

Examples

full <- rbind(c(0, 1, 1, 1),
              c(0, 0, 1, 1),
              c(0, 0, 0, 1),
              c(0, 0, 0, 0))
chain <- rbind(c(0, 1, 0, 0),
               c(0, 0, 1, 0),
               c(0, 0, 0, 1),
               c(0, 0, 0, 0))
identical(ancestor_aid(full, chain, "from row to column"), c(0/12, 0))

Optimal Adjustment Identification Distance between two DAG / CPDAG adjacency matrices

Description

Computes the optimal adjustment intervention distance between the true g_true DAG or CPDAG and an estimated g_guess DAG or CPDAG.

Usage

oset_aid(g_true, g_guess, edge_direction)

Arguments

g_true

Adjacency matrix of the true graph

g_guess

Adjacency matrix of the guess graph

edge_direction

either "from row to column" or "from column to row"

Details

For details see Henckel, Würtzen, Weichwald (2024) doi:10.48550/arXiv.2402.08616
The source code is available at github.com/CausalDisco/gadjid

Graph inputs are accepted as adjacency matrices of type double. An adjacency matrix for a DAG may only contain 0s and 1s. An adjacency matrix for a CPDAG may only contain 0s, 1s and 2s. DAG and CPDAG inputs are validated for acyclicity. However, for CPDAG inputs, the user needs to ensure the adjacency matrix indeed codes a valid CPDAG (instead of just a PDAG).

If edge_direction="from row to column", then a 1 in row r and column c codes a directed edge ‘r → c’; if edge_direction="from column to row", then a 1 in row r and column c codes a directed edge ‘c → r’; for either setting of edge_direction, a 2 in row r and column c codes an undirected edge ‘r – c’ (an additional 2 in row c and column r is ignored; one of the two entries is sufficient to code an undirected edge).

Value

2-element vector of type double
c(normalized error in [0,1], total number of errors)

References

L Henckel, T Würtzen, S Weichwald. "Adjustment Identification Distance: A gadjid for Causal Structure Learning." Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence (UAI), 2024. doi:10.48550/arXiv.2402.08616

Examples

full <- rbind(c(0, 1, 1, 1),
              c(0, 0, 1, 1),
              c(0, 0, 0, 1),
              c(0, 0, 0, 0))
chain <- rbind(c(0, 1, 0, 0),
               c(0, 0, 1, 0),
               c(0, 0, 0, 1),
               c(0, 0, 0, 0))
identical(oset_aid(full, chain, "from row to column"), c(3/12, 3))

Parent Adjustment Identification Distance between two DAG / CPDAG adjacency matrices

Description

Computes the parent adjustment intervention distance between the true g_true DAG or CPDAG and an estimated g_guess DAG or CPDAG.

Usage

parent_aid(g_true, g_guess, edge_direction)

Arguments

g_true

Adjacency matrix of the true graph

g_guess

Adjacency matrix of the guess graph

edge_direction

either "from row to column" or "from column to row"

Details

For details see Henckel, Würtzen, Weichwald (2024) doi:10.48550/arXiv.2402.08616
The source code is available at github.com/CausalDisco/gadjid

Graph inputs are accepted as adjacency matrices of type double. An adjacency matrix for a DAG may only contain 0s and 1s. An adjacency matrix for a CPDAG may only contain 0s, 1s and 2s. DAG and CPDAG inputs are validated for acyclicity. However, for CPDAG inputs, the user needs to ensure the adjacency matrix indeed codes a valid CPDAG (instead of just a PDAG).

If edge_direction="from row to column", then a 1 in row r and column c codes a directed edge ‘r → c’; if edge_direction="from column to row", then a 1 in row r and column c codes a directed edge ‘c → r’; for either setting of edge_direction, a 2 in row r and column c codes an undirected edge ‘r – c’ (an additional 2 in row c and column r is ignored; one of the two entries is sufficient to code an undirected edge).

Value

2-element vector of type double
c(normalized error in [0,1], total number of errors)

References

L Henckel, T Würtzen, S Weichwald. "Adjustment Identification Distance: A gadjid for Causal Structure Learning." Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence (UAI), 2024. doi:10.48550/arXiv.2402.08616

Examples

full <- rbind(c(0, 1, 1, 1),
              c(0, 0, 1, 1),
              c(0, 0, 0, 1),
              c(0, 0, 0, 0))
chain <- rbind(c(0, 1, 0, 0),
               c(0, 0, 1, 0),
               c(0, 0, 0, 1),
               c(0, 0, 0, 0))
identical(parent_aid(full, chain, "from row to column"), c(4/12, 4))

Structural Hamming Distance between two DAG / CPDAG adjacency matrices

Description

Computes the structural Hamming distance between the true g_true PDAG and an estimated g_guess PDAG.

Usage

shd(g_true, g_guess)

Arguments

g_true

Adjacency matrix of the true partially directed acyclic graph

g_guess

Adjacency matrix of the guess partially directed acyclic graph

Details

For details see Henckel, Würtzen, Weichwald (2024) doi:10.48550/arXiv.2402.08616
The source code is available at github.com/CausalDisco/gadjid

Graph inputs are accepted as adjacency matrices of type double. An adjacency matrix for a PDAG may only contain 0s, 1s and 2s. PDAG are validated for acyclicity.

A 1 in row r and column c codes a directed edge and a 1 in row c and column r codes a directed edge in reverse direction; a 2 in row r and column c codes an undirected edge ‘r – c’ (an additional 2 in row c and column r is ignored; one of the two entries is sufficient to code an undirected edge).

Value

2-element vector of type double
c(normalized error in [0,1], total number of errors)

Examples

full <- rbind(c(0, 1, 1, 1),
              c(0, 0, 1, 1),
              c(0, 0, 0, 1),
              c(0, 0, 0, 0))
chain <- rbind(c(0, 1, 0, 0),
               c(0, 0, 1, 0),
               c(0, 0, 0, 1),
               c(0, 0, 0, 0))
identical(shd(full, chain), c(3/6, 3))

Structural Identification Distance between two DAG adjacency matrices

Description

Computes the structural intervention distance (SID), between the true g_true DAG and an estimated g_guess DAG.

Usage

sid(g_true, g_guess, edge_direction)

Arguments

g_true

Adjacency matrix of the true directed acyclic graph

g_guess

Adjacency matrix of the guess directed acyclic graph

edge_direction

either "from row to column" or "from column to row"

Details

Since the Parent-AID reduces to the SID in the special case of DAG inputs and is efficiently implemented using reachability algorithms, it offers a faster way to calculate the SID; see also Henckel, Würtzen, Weichwald (2024) doi:10.48550/arXiv.2402.08616. The example below can be compared to 

library("SID")
system.time(structIntervDist(random_dag(20), random_dag(20)))

For details see Henckel, Würtzen, Weichwald (2024) doi:10.48550/arXiv.2402.08616
The source code is available at github.com/CausalDisco/gadjid

Graph inputs are accepted as adjacency matrices of type double. An adjacency matrix for a DAG may only contain 0s and 1s. DAG inputs are validated for acyclicity.

If edge_direction="from row to column", then a 1 in row r and column c codes a directed edge ‘r → c’; if edge_direction="from column to row", then a 1 in row r and column c codes a directed edge ‘c → r’.

Value

2-element vector of type double
c(normalized error in [0,1], total number of errors)

References

L Henckel, T Würtzen, S Weichwald. "Adjustment Identification Distance: A gadjid for Causal Structure Learning." Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence (UAI), 2024. doi:10.48550/arXiv.2402.08616

J Peters,P Bühlmann. "Structural intervention distance for evaluating causal graphs." Neural Compututation 27(3), 771–799, 2015. doi:10.1162/NECO_a_00708

Examples

random_dag <- function(n, p=0.1) {
    P <- sample(n)
    m <- matrix(0, n, n)
    m[upper.tri(m)] <- rbinom(n*(n-1)/2, 1, p)
    m[P, P]
}

system.time(sid(random_dag(400), random_dag(400), "from row to column"))

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.