SELF
Overview
Provides the SELF criteria to learn causal structure.
Details of the algorithm can be found in "SELF: A Structural Equation Embedded Likelihood Framework for Causal Discovery" (AAAI2018).
Installation
install.packages("SELF")
Quick Start
This package contain the data synthetic process and the casual structure learning algorithm. Here are some examples to make a quick start:
#x->y->z
set.seed(0)
x=rnorm(4000)
y=x^2+runif(4000,-1,1)*0.1
z=y^2+runif(4000,-1,1)*0.1
data=data.frame(x,y,z)
fhc(data,gamma=10,booster = "gbtree")
#x->y->z linear data
set.seed(0)
x=rnorm(4000)
y=3*x+runif(4000,-1,1)*0.1
z=3*y+runif(4000,-1,1)*0.1
data=data.frame(x,y,z)
fhc(data,booster = "lm")
#RandomGraph linear data
set.seed(0)
G=randomGraph(dim=10,indegree=1.5)
data=synthetic_data_linear(G=G,sample_num=4000)
fitG=fhc(data,booster = "lm")
indicators(fitG,G)