IDA_stable: Estimate Total Causal Effects

In ParallelPC: Paralellised Versions of Constraint Based Causal Discovery Algorithms

Description

This the stable version (using stable-PC for structure learning) of the IDA algorithm in the pcalg package.

Usage

IDA_stable(datacsv, cause, effect, pcmethod, alpha)

Arguments

datacsv	The dataset in csv format with rows are samples and columns are variables
cause	The number of integer positions of the cause variables in the dataset
effect	The number of integer positions of the target variables in the dataset.
pcmethod	Character string specifying method; the default, "stable", provides an order-independent skeleton. See Colombo, 2014.
alpha	significance level (number in (0; 1) for the individual conditional independence tests.

Value

A matrix that shows the causal effects (minimum of all possible effects) of the causes (columns) on the effects (rows).

References

1. Marloes H Maathuis, Markus Kalisch, Peter Buhlmann, et al. Estimating high-dimensional intervention effects from observational data. The Annals of Statistics, 37(6A):3133-3164,2009.

2. Diego Colombo and Marloes H Maathuis. Order-independent constraint-based causal structure learning. The Journal of Machine Learning Research, 15(1):3741-3782, 2014.

Examples

##########################################
## Using IDA_stable
##########################################
library(pcalg)
data("gmI")
datacsv <- cov(gmI$x)
IDA_stable(datacsv,1:2,3:4,"stable",0.01)

ParallelPC documentation built on May 2, 2019, 9:14 a.m.