disco_note

internal

Tools for causal structure learning from observational data, with emphasis on temporally ordered variables.
The package implements the Temporal Peter–Clark (TPC) algorithm (Petersen, Osler & Ekstrøm, 2021;
<doi:10.1093/aje/kwab087>), the Temporal Greedy Equivalence Search (TGES) algorithm (Larsen, Ekstrøm & Petersen,
2025; <doi:10.48550/arXiv.2502.06232>) and Temporal Fast Causal Inference (TFCI).
It provides a unified framework for specifying background knowledge,
which can be incorporated into the implemented algorithms from the R packages 'bnlearn' (Scutari, 2010;
<doi:10.18637/jss.v035.i03>) and
'pcalg' (Kalish et al., 2012; <doi:10.18637/jss.v047.i11>), as well as the Java library 'Tetrad'
(Scheines et al., 1998; <doi:10.1207/s15327906mbr3301_3>).
The package further includes utilities for visualization, comparison, and evaluation of graph structures,
facilitating performance evaluation and methodological studies.

Bjarke Hautop Kristensen

causalDisco

Tools for Causal Discovery on Observational Data

Frederik Fabricius-Bjerre

Anne Helby Petersen

Claus Thorn Ekstrøm

Tobias Ellegaard Larsen

disco_note function

While it is possible to call the function returned directly with a data frame,
we recommend using <code>disco()</code>. This provides a consistent interface and handles knowledge
integration.

Recommendation

Disco Recommendation Note — disco_note

While it is possible to call the function returned directly with a data frame,
we recommend using <code>disco()</code>. This provides a consistent interface and handles knowledge
integration.

Disco Recommendation Note

disco_note: Disco Recommendation Note

Description

Arguments

Recommendation