OCT

Out-of-sample Causal Tuning

This repository contains the code for the paper:
K. Biza, I. Tsamardinos, S. Triantafillou, Out-of-sample Tuning for Causal Discovery, Manuscript submitted for publication, 2021

It is based on our previous work:
K. Biza, I. Tsamardinos, S. Triantafillou, Tuning Causal Discovery Algorithms, Proceedings of the Tenth International Conference on Probabilistic Graphical Models, 2020

Contact: konbiza@gmail.com

Experimental_setup.m

Set the parameters for the experiment.

• Nnets : number of repetitions

Causal hyper-parameters

• cdParam.alpha : significance level
• cdParam.penaltyDiscount
• cdParam.structurePrior
• cdParam.algs : causal discovery algorithms {'pc','cpc', 'pcstable', 'cpcstable', 'fges', 'lingam', 'fci', 'fcimax', 'rfci', 'gfci', 'mmhc','m3hc', 'full', 'empty'}
• cdParam.indTests : independence tests {'fisher', 'cci', 'cg_lrt', 'dg_lrt', 'chisquare', 'gsquare'}
• cdParam.scores : {'sembic', 'bdeu', 'discreteBic', 'cg_bic', 'dg_bic'}

Simulation

Simulate data or apply the causal-based simulation

• causalbasedSim=false;

Causal-based Simulation

• simParam.nameData : the name of the real dataset {Iris, Wine, HeartDisease, WineQualityRed, BreastCancerWisconsin, Car, Abalone, ForestFire, StudentMat}
• simParam.Nsamples : the number of samples to resimulate

Data Simulation

• graphInfo

dataType	causalGraph	graphType
continuous	DAG	ConDag
categorical	DAG	CatDag
mixed	DAG	MixDag
continuous	MAG	ConMag
categorical	MAG	CatMag
mixed	MAG	MixMag

• simParam.Nnodes : number of nodes
• simParam.Nedges : number of edges
• simParam.Nsamples: number of samples
• simParam.Nlatent : number of latent variables (if causalGraph=MAG)

In addition, for mixed data:

• simParam.method : {'CondGaus', 'LeeHastie'}

and for continuous data:

• simParam.func : causal functional relations {'sumX', 'sumX2', 'sumX-1', 'prodX', 'tanh', 'sumabsX', 'logX', 'exp05', 'exp15', 'logcosh', 'prode'}
• simParam.errorDist : error distribution {'gaussian', 'uniform'}

SID

• computeSid=false

To compute SID you need first to modify StructInterventionalDist.m and sidR.R

StructInterventionalDist.m
line 11: Change the path and the R version if needed. Example:
rscript = '"C:\Program Files\R\R-3.6.3\bin\Rscript.exe" ';

sidR.R
line 1: Change the path. Example:
setwd('C:/Users/konstantina/OCT/Functions/SidMetric')

We use the SID package

TETRAD

We use the tetrad project for the simulation of mixed data, causal discovery algorithms (except MMHC, M3HC), BIC, AIC, CG, and DG scores.
For more information read also : http://cmu-phil.github.io/tetrad/manual/
Please download the jar file "tetrad-gui-6.8.1-launch.jar" and add it in the tetradJar folder.

SCORE=2L-penDiscount*ln(n)*k

• if penDiscount=1 : BIC
• if penDiscount=2/ln(n) : AIC

CG=2(L+structurePrior)-penDiscount*ln(n)*k

DG=2(L+structurePrior)-penDiscount*ln(n)*k

Higher is better for all scores (BIC, AIC, CG, DG)

Notation

DAG
(i, j)=1 and (j, i)=0 : i-->j

PDAG
(i, j)=1 and (j, i)=0 : i-->j
(i, j)=1 and (j, i)=1 : i---j

MAG
(i, j)=2 and (j, i)=3 : i-->j
(i, j)=2 and (j, i)=2 : i<->j

PAG
(i, j)=2 and (j, i)=3 : i-->j
(i, j)=2 and (j, i)=2 : i<->j
(i, j)=2 and (j, i)=1 : io->j
(i, j)=1 and (j, i)=1 : io-oj

Tuning.m

Evaluate the performance of the tuning methods.