jointIDA_direct {ParallelPC}

Estimate Total Causal Effects of Joint Interventions

Description

This is the parallelised version of the jointIDA (stable) algorithm in the pcalg package.

Usage

jointIDA_direct(datacsv, cause, effect, method = c("min", "max", "median"),
  pcmethod = "stable", alpha, num.cores = 1, mem.efficient = FALSE,
  technique = c("RRC", "MCD"))

Arguments

datacsv

The dataset in the csv format with rows are samples and columns are the variables.

cause

The number of integer positions of the intervention variables in the dataset.

effect

the integer position of the target variable in the dataset.

method

the method of calculating the final effect from multiple possible effects, e.g. min, max, median

pcmethod

Character string specifying the method of the PC algorithm, e.g. stable for stable-PC, and parallel for parallel-PC.

alpha

significance level (number in (0; 1) for the conditional independence tests.

num.cores

The numbers of cores CPU to run the algorithm

mem.efficient

If TRUE, uses less amount of memory at any time point while running the algorithm

technique

The character string specifying the technique that will be used to estimate the total joint causal effects in the pcalg package. RRC for Recursive regression for causal effects MCD for Modifying the Cholesky decomposition

Value

A matrix that shows the direct causal effects (minimum of all possible effects) of the (first) cause (columns) on the effects (rows)