OrdCD is an R package (available on CRAN) for discovering causality from observational ordinal categorical data, developed and maintained by Yang Ni at Texas A&M University.

The package can also be downloaded at https://web.stat.tamu.edu/~yni/files/OrdCD_1.1.0.tar.gz.


Ni, Y., & Mallick, B. (2022). Ordinal Causal Discovery. Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, (UAI 2022), PMLR 180:1530–1540.

A small simulation example

This example generates a network with 5 nodes, 3 categoreis, and 1000 observations. The true graph is a Markov chain. ```{r dataload,echo=TRUE, warning=FALSE, message=TRUE } set.seed(2020) n=1000 #sample size q=5 #number of nodes y = u = matrix(0,n,q) u[,1] = 4rnorm(n) y[,1] = (u[,1]>1) + (u[,1]>2) for (j in 2:q){ u[,j] = 2y[,j-1] + rnorm(n) y[,j]=(u[,j]>1) + (u[,j]>2) } A=matrix(0,q,q) #true DAG adjacency matrix A[2,1]=A[3,2]=A[4,3]=A[5,4]=1 y=as.data.frame(y) for (j in 1:q){ y[,j]=as.factor(y[,j]) }

G=OCD(y) #estimated DAG adjacency matrix print(A) print(G) ```