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rcit's Introduction

RCIT and RCoT

This is an R package implementing the Randomized Conditional Independence Test (RCIT) and the Randomized conditional Correlation Test (RCoT). As a general rule, RCoT is superior to RCIT for conditional independence testing.

The academic article describing RCIT and RCoT in detail can be found here. Please cite the article if you use any of the code in this repository.

Installation

The package depends on the MASS and momentchi2 packages on CRAN, so please install these first. Then:

library(devtools)

install_github("ericstrobl/RCIT")

library(RCIT)

RCIT(rnorm(1000),rnorm(1000),rnorm(1000))

RCoT(rnorm(1000),rnorm(1000),rnorm(1000))

RCoT(rnorm(1000),rnorm(1000),matrix(rnorm(2000),1000,2)) # matrices for more than one variable

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rcit's Issues

python code

Hi Excuse me, is there any python code about this algorithm?

RCIT/KCIT for causal discovery with mixed data

Hi,

I've been told that KCIT (and therefore I presume RCIT/RCoT) can be used with mixed continuous and discrete data. However, playing around with the package this doesn't seem to work. Is there something I need to adjust to make the tests work with mixed data?

Also, if using mixed data is possible, how could KCIT or RCIT then be used with an algorithm like FCI for causal discovery? The code showing how this was done for the causal discovery experiments in your paper doesn't seem to be available in this repo. Could KCIT or RCIT be used as a CI test in the FCI function from pcalg, for instance?

Many thanks.

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