ParallelPC (version 1.2)

IDA_stable: Estimate Total Causal Effects

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)