Provides the SELF criteria to learn causal structure. Please cite "Ruichu Cai, Jie Qiao, Zhenjie Zhang, Zhifeng Hao. SELF: Structural Equational Embedded Likelihood Framework for Causal Discovery. AAAI. 2018."
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).
install.packages("SELF")
This package contain the data synthetic process and the casual structure learning algorithm. Here are some examples to make a quick start:
#x->y->zset.seed(0)x=rnorm(4000)y=x^2+runif(4000,-1,1)*0.1z=y^2+runif(4000,-1,1)*0.1data=data.frame(x,y,z)fhc(data,gamma=10,booster = "gbtree") #x->y->z linear dataset.seed(0)x=rnorm(4000)y=3*x+runif(4000,-1,1)*0.1z=3*y+runif(4000,-1,1)*0.1data=data.frame(x,y,z)fhc(data,booster = "lm") #RandomGraph linear dataset.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)
maxite
to randomGraphNEWS.md
file to track changes to the package.