| IDA_stable {ParallelPC} | R Documentation |
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)