| Type: | Package |
| Title: | Causal Discovery for Categorical Data with Label Permutation |
| Version: | 1.0.0 |
| Date: | 2022-09-23 |
| Description: | Discover causality for bivariate categorical data. This package aims to enable users to discover causality for bivariate observational categorical data. See Ni, Y. (2022) <doi:10.48550/arXiv.2209.08579> "Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation. Advances in Neural Information Processing Systems 35 (in press)". |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.2 |
| Imports: | MASS, combinat, stats |
| URL: | https://github.com/nySTAT/COLP |
| BugReports: | https://github.com/nySTAT/COLP/issues |
| NeedsCompilation: | no |
| Packaged: | 2022-09-27 14:00:47 UTC; yangn |
| Author: | Yang Ni  [aut,
cre] |
| Maintainer: | Yang Ni <yni@stat.tamu.edu> |
| Depends: | R (≥ 3.5.0) |
| Repository: | CRAN |
| Date/Publication: | 2022-09-29 08:40:12 UTC |
Causal Discovery for Bivariate Cateogrical Data
Description
Estimate a causal directed acyclic graph (DAG) for ordinal cateogrical data with greedy or exhaustive search.
Usage
COLP(y, x, algo = "E")
Arguments
y |
factor, a potential effect variable |
x |
factor, a potential cause variable |
algo |
exhaustive search (algo="E") of category ordering or greedy search (algo="G") |
Value
A list of length 3. cd = 1 if x causes y; cd = 0 otherwise. P is the optimal odering of the effect variable. epsilon is the difference in log-likelihood favoring x causes y.
Examples
fit = COLP(CatPairs[[1]][[1]]$Diffwt,CatPairs[[1]][[1]]$Treat,algo="E")
fit$cd
Categorical Cause-Effect Pairs
Description
Cause-effect pairs extracted from R packages MASS and datasets for which the pairwise causal relationships are clear from the context, and at least one of the variables in each pair is categorical. For non-categorical variable, we discretized it at 5 evenly spaced quantiles.The current version contains 33 categorical cause-effect pairs.
Usage
data(CatPairs)
Format
A list of length 2. The first element is a list of 33 cause-effect pairs as data frames with the first column being the cause and the second column being the effect. The second element is a list of sources of each pair.