TY - JOUR
AU - 
AB - The pcalg package for R can be used for the following two purposes: Causal structure learning and estimation of causal e ects from observational data. In this document, we give a brief overview of the methodology, and demonstrate the package's functionality in both toy examples and applications. Keywords : IDA, PC, RFCI, FCI, do-calculus, causality, graphical model, R. 1. Introduction Understanding cause-e ect relationships between variables is of primary interest in many elds of science. Usually, experimental intervention is used to nd these relationships. In many settings, however, experiments are infeasible because of time, cost or ethical constraints. We therefore consider the problem of inferring causal information from observational data. Under some assumptions, the PC algorithm (see Spirtes, Glymour, and Scheines 2000), the FCI algorithm (see Spirtes et al. 2000 and Spirtes, Meek, and Richardson 1999) and the RFCI algorithm (see Colombo, Maathuis, Kalisch, and Richardson 2012) can infer information about the causal structure from observational data. Thus, these algorithms tell us which variables could or could not be a cause of some variable of interest. They do not, however, give information about the size of the causal e ects. We therefore developed the IDA method (see Maathuis, Kalisch, 
TI - Causal Inference Using Graphical Models with the<i>R</i>Package<b>pcalg</b>
JF - Journal of Statistical Software
DO - 10.18637/jss.v047.i11
DA - 2012-01-01
UR - https://www.deepdyve.com/lp/unpaywall/causal-inference-using-graphical-models-with-the-i-r-i-package-b-pcalg-BRPp5qsGBH
DP - DeepDyve
ER -