ParDAG-class

plot,ParDAG,ANY-method

This virtual base class represents a parametric causal model.

classes

Functions for causal structure
  learning and causal inference using graphical models. The main algorithms
  for causal structure learning are PC (for observational data without hidden
  variables), FCI and RFCI (for observational data with hidden variables),
  and GIES (for a mix of data from observational studies
  (i.e. observational data) and data from experiments
  involving interventions (i.e. interventional data) without hidden
  variables). For causal inference the IDA algorithm and the generalized
  backdoor criterion is implemented.

Markus Kalisch

pcalg

Methods for Graphical Models and Causal Inference

ParDAG-class function

All reference classes extend and inherit methods from <code>"<links4class>envRefClass</links4class>"</code>.

ParDAG-class: Class `"ParDAG"`

Description

Arguments

Extends

Details

See Also