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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).
{r, eval = FALSE} 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:
```{r example} #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=3x+runif(4000,-1,1)0.1 z=3y+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) ```
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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