Markus Kalisch, Martin Maechler, Diego Colombo
Estimation of CPDAG/PAG and causal inference
Standard and robust estimation the equivalence class of a
Directed Acyclic Graph (DAG) via the PC-Algorithm. The
equivalence class is represented by its (unique) Completetd
Partially Directed Acyclic Graph (CPDAG). The function pcSelect
implements variable selection techniques based on the
PC-algorithm. Furthermore, a PAG instead of a CPDAG can be
estimated if latent variables and/or selection variables are
assumed to be present. Functions for causal inference (using
do-calculus of Judea Pearl) are provided for CPDAGs.
methods, MASS, graph, robustbase, Rgraphviz, ggm, mnormt, vcd,
RBGL, sfsmisc

Estimation of CPDAG/PAG and causal inference using the IDA
algorithm
Standard and robust estimation of the equivalence class of
a Directed Acyclic Graph (DAG) via the PC-Algorithm. The
equivalence class is represented by its (unique) Completetd
Partially Directed Acyclic Graph (CPDAG). Furthermore, a PAG
instead of a CPDAG can be estimated if latent variables and/or
selection variables are assumed to be present. Functions for
causal inference using the IDA algorithm (based on do-calculus
of Judea Pearl) are provided for CPDAGs.
abind, corpcor, methods, MASS, graph, Rgraphviz, ggm, vcd,
RBGL, sfsmisc, robustbase

Standard and robust estimation of the equivalence class of
a Directed Acyclic Graph (DAG) via the PC-Algorithm. The
equivalence class is represented by its (unique) Completete
Partially Directed Acyclic Graph (CPDAG). Furthermore, a PAG
instead of a CPDAG can be estimated if latent variables and/or
selection variables are assumed to be present. Functions for
causal inference using the IDA algorithm (based on do-calculus
of Judea Pearl) are provided for CPDAGs.
abind, corpcor, methods, MASS, graph, ggm, vcd, RBGL, sfsmisc,
robustbase

graph, RBGL, ggm, robustbase, graphics, vcd

Standard and robust estimation of the equivalence class of
a Directed Acyclic Graph (DAG) via the PC-Algorithm. The
equivalence class is represented by its (unique) Completete
Partially Directed Acyclic Graph (CPDAG). Furthermore, a PAG
instead of a CPDAG can be estimated if latent variables and/or
selection variables are assumed to be present. FCI and RFCI are
available for estimating PAGs. Functions for causal inference
using the IDA algorithm (based on do-calculus of Judea Pearl)
are provided for CPDAGs.